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  • 51.
    Ranneby, Bo
    et al.
    SLU, Centre of Biostochastics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Estimation of WTP with point and self-selected interval responses2011In: Modern cost-benefit analysis of hydropower conflicts / [ed] Per-Olov Johansson och Bengt Kriström, Edward Elgar Publishing, 2011, p. 65-75Chapter in book (Refereed)
  • 52.
    Ranneby, Bo
    et al.
    SLU, Centre of Biostochastics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. SLU, Centre of Biostochastics.
    Nonparametric and probabilistic classification using NN-balls with environmental and remote sensing applications2011In: Advances in Directional and Linear Statistics: A Festschrift for Sreenivasa Rao Jammalamadaka / [ed] M.T. Wells & A. Sengupta, Heidelberg: Physica Verlag, 2011, p. 201-216Chapter in book (Refereed)
    Abstract [en]

    National and international policies today require environmental follow-upsystems that detect, in a quality assured way, changes over time in land use and landscape indicators. Questions related to environmental health and spatial patterns call for new statistical tools.We present in this chapter some new developments on the classification of land use by using multispectral and multitemporal satellite images, based on techniques of nearest neighbour balls. The probabilistic classifiers introduced are useful for measuring uncertainty at pixel level and obtaining reliable area estimates locally. Also some theoretical considerations for the reference sample plotmethod (today named k-NN method in natural resource applications) are presented.

  • 53.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Stoverud, Karen-Helene
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Segmentation of Motor Unit Territories in Ultrasound Image Sequences of Contracting Skeletal Muscle Tissue2017Conference paper (Other academic)
    Abstract [en]

    Ultrasound medical imaging can be used to visualize and quantify anatomical and functional aspects of internal tissues and organs of the human body. Skeletal muscle tissue is functionally composed by motor units, which are the smallest voluntarily activatable units. In order to capture a transient phenomenon, such as the contraction mechanism, a high sample rate is required. There has been a lot of research on whole-muscle aspects in terms of skeletal muscle contraction characteristics, neuromuscular disorders, and inter-muscle segmentation. Previous studies have shown that small-scale muscle twitches can be detected using ultrasound and there are several reports on ultrasound-based detection of electro-stimulated motor unit activity. However, methods for intra-muscular ultrasound-based analysis of muscle tissue are largely underdeveloped, in particular regarding the level of motor units.Diagnostics of skeletal muscle tissue is based on analyzing features of these units by invasive, non-imaging electrophysiological methods. Here,we make progress by using non-invasive ultrasound imaging to segment motor units, which have the potential to be a non-invasive substitute and where the imaging provides an important contribution.

  • 54.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Department of Clinical Neurophysiology, Department of Neurosciences, University Hospital, Uppsala University, Sweden.
    Stoverud, Karen-Helene
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A Method for Identification of Mechanical Response of Motor Units in Skeletal Muscle Voluntary Contractions using Ultrafast Ultrasound Imaging: Simulations and Experimental Tests2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 50299-50311Article in journal (Refereed)
    Abstract [en]

    The central nervous system coordinates movement through forces generated by motor units (MUs) in skeletal muscles. To analyze MUs function is essential in sports, rehabilitation medicine applications, and neuromuscular diagnostics. The MUs and their function are studied using electromyography. Typically, these methods study only a small muscle volume (1 mm3) or only a superficial (< 1 cm) volume of the muscle. Here we introduce a method to identify so-called mechanical units, i.e., the mechanical response of electrically active MUs, in the whole muscle (4x4 cm, cross-sectional) under voluntary contractions by ultrafast ultrasound imaging and spatiotemporal decomposition. We evaluate the performance of the method by simulation of active MUs' mechanical response under weak contractions. We further test the experimental feasibility on eight healthy subjects. We show the existence of mechanical units that contribute to the tissue dynamics in the biceps brachii at low force levels and that these units are similar to MUs described by electromyography with respect to the number of units, territory sizes, and firing rates. This study introduces a new potential neuromuscular functional imaging method, which could be used to study a variety of questions on muscle physiology that previously were difficult or not possible to address.

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  • 55.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Uppsala University.
    Stoverud, Karen-Helene
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ultrasound-based Imaging of Motor Units in Skeletal Muscle Tissue2018Conference paper (Other academic)
    Abstract [en]

    Neuromuscular diseases hinder muscle function and may be the outcome of damage and dysfunction of the smallest voluntarily activatable units in skeletal muscle tissue, the so-called motor units (MUs). MUs generate electrical signals and analyzing these signals gives a basis to assess and diagnose MUs. The signals are captured using needle electromyography, which is an invasive and non-imaging method. Here, we showultrasound-basedimaging of MUs, via an ultrasound-based spatiotemporal decomposition framework.

  • 56.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Stålberg, Erik
    Uppsala University.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Imaging recruitment of motor units in voluntary skeletal muscle contractions using decomposition and ultrafast ultrasound imaging: A pilot study2020In: 2020 ISEK Virtual Congress Poster Abstract Booklet, International Society of Electrophysiology and Kinesiology , 2020, p. 141-142Conference paper (Other academic)
    Abstract [en]

    Recently our research group demonstrated a method to separate and identify the mechanical response of individual active MUs, from a large part of a muscle (4x4 cm, cross-sectional) under voluntary contractions. The method is based on ultrafast ultrasound imaging and spatiotemporal decomposition. In the present work we aimed to use this method to explore MU territory recruitment patterns at low force levels in the biceps brachii.

  • 57.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of low force voluntary skeletal muscle contractions2022In: BMC Research Notes, E-ISSN 1756-0500, article id 207Article in journal (Refereed)
    Abstract [en]

    Objective: In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantifed using two measures: (1) the similarity of components’ temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the diferent algorithms.

    Results: We found that out of these four algorithms, no algorithm signifcantly improved the motor unit identifcation success compared to stICA using spatial information, which was the best together with stSOBI using either spatialor temporal information. Moreover, there was a strong agreement of detected sets of components between the different algorithms. However, stJADE (using temporal information) provided with complementary successful detections. These results suggest that the choice of decomposition algorithm is not critical, but there may be a methodological improvement potential to detect more motor units

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  • 58.
    Stefan, Löfgren
    et al.
    Institutionen för vatten och miljö, SLU.
    Nisell, Jakob
    Institutionen för vatten och miljö, SLU.
    Yu, Jun
    Centre of Biostochastics, SLU.
    Ranneby, Bo
    Centre of Biostochastics, SLU.
    N- och P-halterna i skog, myr och fjäll våren 2011 i Dalälven, Viskan, Ätran, Nissan och Lagan: projekt för att förbättra skattningarna av typhalter inför PLC62011Report (Other academic)
    Abstract [en]

    As part of an earlier pilot project (SMED Report No. 52 2011) and based on data from the national forest survey (RIS) and satellite scenes, the statistical method Probabilistic classifier was used for classifying the forest status of forest land, peat land and mountain areas. Riparian forests were defined based on the virtual streamwater network (VIVAN). Based on data from 200 randomly selected head-waters of the rivers Dalälven, Viskan, Ätran, Nissan and Lagan and the forest sta-tus in riparian and more remote forests, models were created to estimate summer type concentrations of N and P in stream water. The explanation power of the Tot-N and Tot-P models was significantly higher than those used in connection with PLC5 and the modelled type concentrations, representing summer conditions, were significantly higher than those used in PLC5.

    Within this new project, water samples were collected and analyzed in spring 2011 from the 200 randomly selected headwaters of river Dalälven and in western Sweden. The measured summer and spring concentration levels were compared and, based on the same methodology as in the previous pilot project, models were created to estimate the springtime Tot-N and Tot-P type concentrations in stream water. Additionally, water samples were collected in September and November 2011 (in progress), but funding for the chemical analyses are lacking.

    The spring survey showed lower Tot-N and Tot-P concentrations than during the summer inventories, especially in the Dalälven area. At spring, the median values were just over half the summer values. In western Sweden, the differences between seasons were much smaller and based on median values the spring concentrations were 14% and 25% lower for Tot-N and Tot-P, respectively. Compared with the PLC5 type concentrations, the spring values in the Dalälven area were about 35% and 45% lower for Tot-N and Tot-P, respectively, while on the West Coast they were 24-51% higher for Tot-N and 62% higher for Tot-P. Similarly to the summer survey, the spring inventory indicates that the PLC5 type concentrations underes-timated forest leaching of both Tot-N and Tot-P in Western Sweden, while the results from the Dalälven area instead indicates an overestimate. Type concentra-tions produced by models with data only from spring and summer cannot be uncrit-ically used for source apportionment modelling and as a basis for estimating N and P retention from the headwaters to the sea. This requires models based on water chemical data from all seasons.

    The spring survey also showed that the explanation power of the models is signifi-cantly higher than for those used in PLC5, suggesting that there are relations between catchment characteristics and N and P concentrations and that Probabilistic classifier is a useful method for estimating these properties. During both summer and spring, the models explained approximately 60% and 31% of the variation for Tot-N and Tot-P, respectively. The models indicate that the nutrient losses are controlled primarily by the amount of biomass in spring, while growth is ruling during summer.

    Given the typically high water discharge in spring and autumn and the resulting impact on the nutrient transport calculations (= concentration x discharge), there are good reasons to take into account the spatial concentration variations during fall in the PLC6 source apportionments by improving the N and P type concentrations estimates. The chemical analysis of the project collected autumn samples should therefore be funded. In addition, similar surveys should be conducted in northern Sweden in order to improve the estimates of N and P losses from forest land, peat land and mountain areas also in this part of the country.

  • 59.
    Söderström, Tor
    et al.
    Umeå University, Faculty of Social Sciences, Department of Education.
    Fahlén, Josef
    Umeå University, Faculty of Social Sciences, Department of Education.
    Ferry, Magnus
    Umeå University, Faculty of Social Sciences, Department of Education.
    Yu, J
    Umeå University, Faculty of Science and Technology, Department of Science and Mathematics Education.
    Participation in non-elite sport in early adulthood: the impact of athletic ability in childhood and adolescence2015In: 20 th annual Congress of the European college of Sport Science, (ECSS), 24th - 27th June 2015, Malmö – Sweden: book of abstracts / [ed] Radmann, A., Hedenborg, S., Tsolakidis, E., 2015, p. 294-Conference paper (Other academic)
    Abstract [en]

    Introduction Understandings of, explanations to, and predictors of adult participation in organized sport, on the one hand, and expert performances in organized sport as adults on the other hand have received a lot of attention as separate entities by scholars in the areas of sport participation and sport expertise alike. Although scholars in both fields share an interest in tracing explanatory factors and predictors, it is evident that sport participation research has not investigated the impact of factors that are in focus in sport expertise research and vice versa. Thus, in this paper we aim to explore relationships between sport performance during childhood and adolescence and participation in sport in adulthood.

    Methods Data were derived from Web-based questionnaires completed by university students between 2005 and 2012. In total, 572 students (290 men and 282 women) completed the questionnaires. These students were at the beginning of their studies in sports science (n=357) and physical education teacher education (n=215). The questionnaire gathered information about the following topics: • Biographical data: date of birth; • Sport debut: age they started to participate in organized club sports; • Sport performance: self-estimated sporting skills and participation in regional talent groups during childhood and adolescence. • Sport involvement: previous and present involvement in organized club sports;

    Results Results from questionnaires reveal that early sport debut and date of birth positively correlate to strong sport performances during childhood and being selected for talent groups. These variables, in turn, are positively correlated to strong sport performances during adolescence and being selected for talent groups. Strong sport performances during adolescence do not correlate to expert performance as adults. However, strong sport performances during adolescence are positively correlated to sports club membership as adults.

    Discussion These results suggest a need to further explore how factors found to be important for elite sport practice and expert performance, also influence non-elite sport participation in adulthood. Our findings suggest that talent development system selecting children and youth to develop their abilities and to become elite athletes, not only develops potential elite athletes but also shapes the larger recruitment of adults to sport at non-elite levels and participation in general exercise activities.

  • 60.
    Söderström, Tor
    et al.
    Umeå University, Faculty of Social Sciences, Department of Education.
    Fahlén, Josef
    Umeå University, Faculty of Social Sciences, Department of Education.
    Ferry, Magnus
    Umeå University, Faculty of Social Sciences, Department of Education.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Athletic ability in childhood and adolescence as a predictor of participation in non-elite sports in young adulthood2018In: Sport in Society: Cultures, Media, Politics, Commerce, ISSN 1743-0437, E-ISSN 1743-0445, Vol. 21, no 11, p. 1686-1703Article in journal (Refereed)
    Abstract [en]

    In this article, we contribute to the discussion on factors affecting adult participation in organized sport. To this end, we examine whether explanations regarding sport expertise can also add to the understanding of non-elite-level sport participation in young adulthood. Results from questionnaires (n = 572) revealed that date of birth and early sport debut positively correlated to strong sport performance during childhood, which, in turn, were correlated to strong sport performance and being selected for talent groups during adolescence. Finally, strong sport performance during adolescence was positively correlated to sports club membership as young adults. As relative age effects seem to remain throughout childhood and adolescence, we conclude that the underlying variable that affects the selection process and sport participation in young adulthood is date of birth. The results indicate that being active in sport as young adults is contingent on sport-specific variables previously not investigated in research on sport participation.

  • 61.
    Teterukovskiy, Alex
    et al.
    SLU, Centre of Biostochastics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Contextual reclassification of multispectral images: a Markov Random Field approach2002In: Information Processes, ISSN 1819-5822, Vol. 2, no 1, p. 12-21Article in journal (Refereed)
    Abstract [en]

    This work presents methods for multispectral image classification using the contextual classifiersbased on Markov Random Field (MRF) models. Performance of some conventional classification methods is evaluated, through a Monte Carlo study, with or without using the contextual reclassification. Spatial autocorrelation is present in the computer-generated data on a true scene. The total misclassification rates for varying strengths of autocorrelation and for different methods are compared. The results indicate that the combination of the spectral-contextual classifiers can improve to a great extent the accuracyof conventional non-contextual classification methods. It is also shown how the most complicated cases can be handled by the Gibbs sampler.

  • 62.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Fonseca, Ricardo M.
    Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
    Rutledge, Kendall
    Novia University of Applied Sciences, Vaasa, Finland.
    Martin-Torres, Javier
    Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia using the WRF model2020In: Advances in Atmospheric Sciences, ISSN 0256-1530, E-ISSN 1861-9533, Vol. 37, p. 57-74Article in journal (Refereed)
    Abstract [en]

    An accurate simulation of air temperature at local-scales is crucial for the vast majority of weather and climate applications. In this work, a hybrid statistical-dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean, minimum and maximum air temperatures to investigate the quality of local scale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute (FMI) over a near-coastal region of western Finland. The dynamical downscaling is performed with the Weather Research and Forecasting (WRF) model, and the statistical downscaling method implemented is the Cumulative Distribution Function-transform (CDF-t). The CDF-t is trained using 20-years of WRF-downscaled Climate Forecast System Reanalysis (CFSR) data over the region at 3 km spatial resolution for the central month of each season. The performance of the two methods is assessed qualitatively, by inspection of quantile-quantile (Q-Q) plots, and quantitatively, through the Cramer-von Mises (CvM), mean absolute error (MAE), and root-mean-square Error (RMSE) diagnostics. The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling (for all seasons). The hybrid method proved to be less computationally expensive, and also to give more skillful temperature forecasts (at least for the Finnish near-coastal region).

  • 63.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Fonseca, Ricardo M.
    Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
    Rutledge, Kendall
    Novia University of Applied Sciences, Vaasa, Finland.
    Martin-Torres, Javier
    Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Weather Simulation Uncertainty Estimation using Bayesian Hierarchical Model2019In: Journal of Applied Meteorology and Climatology, ISSN 1558-8424, E-ISSN 1558-8432, Vol. 58, no 3, p. 585-603Article in journal (Refereed)
    Abstract [en]

    Estimates of the uncertainty of model output fields (e.g. 2-meter temperature, surface radiation fluxes or wind speed) are of great value to the weather and climate communities. The traditional approach for the uncertainty estimation is to conduct an ensemble of simulations where the model configuration is perturbed, and/or different models are considered. This procedure is very computationally expensive and may not be feasible in particular for higher resolution experiments. In this paper a new method based on Bayesian Hierarchical Models (BHM) that requires just one model run is proposed. It is applied to the Weather Research and Forecasting (WRF) model’s 2-meter temperature in the Botnia-Atlantica region in Scandinavia for a 10-day period in the winter and summer seasons. For both seasons, the estimated uncertainty using the BHM is found to be comparable to that obtained from an ensemble of experiments in which different Planetary Boundary Layer (PBL) schemes are employed. While WRF-BHM is not capable of generating the full set of products obtained from an ensemble of simulations, it can be used to extract commonly used diagnostics including the uncertainty estimation which is the focus of this work. The methodology proposed here is fully general and can easily be extended to any other output variable and numerical model.

  • 64.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Liu, Xijia
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRIManuscript (preprint) (Other academic)
    Abstract [en]

    The purpose of this study is to investigate a method, using simulations, to improve contrast agent quantification in Dynamic Contrast Enhanced MRI. Bayesian hierarchical models (BHMs) are applied to smaller images (10×10×10) such that spatial information can be incorporated. Then exploratory analysis is done for larger images (64×64×64) by using maximum a posteriori (MAP).

    For smaller images: the estimators of proposed BHMs show improvements in terms of the root mean squared error compared to the estimators in existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperforms Besag models. For larger images: MAP estimators also show improvements by assigning Leroux prior.

  • 65.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using Bayesian hierarchical model2016In: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling / [ed] A. Iftimi, J. Mateu and F. Montes, 2016, p. 217-217Conference paper (Other academic)
  • 66.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI2021In: Frontiers in Signal Processing, E-ISSN 2673-8198, Vol. 1, p. 12article id 727387Article in journal (Refereed)
    Abstract [en]

    The purpose of this work is to investigate spatial statistical modelling approaches to improve contrast agent quantification in dynamic contrast enhanced MRI, by utilising the spatial dependence among image voxels. Bayesian hierarchical models (BHMs), such as Besag model and Leroux model, were studied using simulated MRI data. The models were built on smaller images where spatial dependence can be incorporated, and then extended to larger images using the maximum a posteriori (MAP) method. Notable improvements on contrast agent concentration estimation were obtained for both smaller and larger images. For smaller images: the BHMs provided substantial improved estimates in terms of the root mean squared error (rMSE), compared to the estimates from the existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperformed Besag models with two different dependence structures. Specifically, the Besag models increased the estimation precision by 27% around the peak of the dynamic curve, while the Leroux model improved the estimation by 40% at the peak, compared with the existing estimation method. For larger images: the proposed MAP estimators showed clear improvements on rMSE for vessels, tumor rim and white matter.

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  • 67.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Granlöf, Markus
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Effects of winter climate on delays of high speed passenger trains in Botnia-Atlantica region2021In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 18, article id 100251Article in journal (Refereed)
    Abstract [en]

    Harsh winter climate can cause various problems for both public and private sectors in Sweden, especially in the northern part for railway industry. To have a better understanding of winter climate impacts, this study investigates effects of the winter climate including ice/snow precipitation on the performance of high speed passenger trains in the Botnia-Atlantica region. The investigation is done with train operational data together with simulated weather data fromthe Weather Research and Forecast model over January–February 2017.

    Two different measurements of the train performance are analysed. One is primary delay which measures the increment in delay in terms of running time within two consecutive measuring spots, the other is arrival delay which is the delay in terms of arrival time at each measuring spot compared to the schedule. Primary delay is investigated through a Cox model and the arrival delay is studied using a Markov chain model.

    The results show that the weather variables have impacts on the train performance. Therein temperature and humidity have significant impacts on both the occurrence of primary delay and the transition intensities between arrival delay and non-delay.

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  • 68.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Granlöf, Markus
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Effects of winter climate on high speed passenger trains in Botnia-Atlantica region2020Manuscript (preprint) (Other academic)
    Abstract [en]

    Harsh winter climate can cause various problems for both public and private sectors in Sweden, especially in the northern part for railway industry. To have a better understanding of winter climate impacts, this study investigates effects of the winter climate including atmospheric icing on the performance of high speed passenger trains in the Botnia-Atlantica region. The investigation is done with train operational data together with simulated weather data from the Weather Research and Forecast model over January - February 2017.

    Two different measurements of the train performance are analysed. One is cumulative delay which measures the increment in delay in terms of running time within two consecutive measuring spots, the other is current delay which is the delay in terms of arrival time at each measuring spot compared to the schedule. Cumulative delay is investigated through a Cox model and the current delay is studied using a Markov chain model.

    The results show that the weather factors have impacts on the train performance. Therein temperature and humidity have significant impacts on both the occurrence of cumulative delay and the transition probabilities between (current) delayed and non-delayed states.

  • 69.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Mantas-Nakhai, Roberto
    Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Statistical learning for train delays and influence of winter climate and atmospheric icing2023In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 26, p. 13article id 100388Article in journal (Refereed)
    Abstract [en]

    This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains. Inhomogeneous Markov chain model and stratified Cox model were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling used covariates weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that temperature, snow depth, ice/snow precipitation, and train operational direction significantly impacted the arrival delay. Further, by partitioning the train line into three segments as per transition intensity, the model identified that the middle segment had the highest chance of a transfer from punctuality to delay, and the last segment had the lowest probability of recovering from delayed state. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method, which indicated that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line. With the model performance, the fitted model could be beneficial for both travellers to plan their trips reasonably and railway operators to design more efficient and wiser train schedules as per weather condition.

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  • 70.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Sparsity Estimation in Compressive Sensing2017In: 3rd International Researchers, statisticians and young statisticians congress, 24-26 May, Selçuk University: Abstract Book, 2017, p. 138-138Conference paper (Other academic)
  • 71.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Train performance analysis using heterogeneous statistical models2021In: Atmosphere, ISSN 2073-4433, E-ISSN 2073-4433, Vol. 12, no 9, p. 12article id 1115Article in journal (Refereed)
    Abstract [en]

    This study investigated the eect of harsh winter climate on the performance of high speed passenger trains in northern Sweden. Novel approaches based on heterogeneous statistical models were introduced to analyse the train performance in order to take the time-varying risks of train delays into consideration. Specically, stratied Cox model and heterogeneous Markov chain model were used for modelling primary delays and arrival delays, respectively. Our results showed that the weather variables including temperature, humidity, snow depth, and ice/snow precipitation have signicant impact on the train performance.

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  • 72.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Bayesian sparsity estimation in compressive sensing with application to MR images2019In: Communications in Statistics: Case Studies, Data Analysis and Applications, ISSN 2373-7484, Vol. 5, no 4, p. 415-431Article in journal (Refereed)
    Abstract [en]

    The theory of compressive sensing (CS) asserts that an unknownsignal x ∈ CN can be accurately recovered from m measurements with m « N provided that x is sparse. Most of the recovery algorithms need the sparsity s = ||x||0 as an input. However, generally s is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real data study, magnetic resonance image data is used as input signal, which becomes sparse after sparsified transformation. The results from the simulation study confirm the theoretical properties of the estimator. In practice, the estimate from a real MR image can be used for recovering future MR images under the framework of CS if they are believed to have the same sparsity level after sparsification.

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  • 73.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Sparsity estimation in compressive sensing with application to MR images2017Manuscript (preprint) (Other academic)
    Abstract [en]

    The theory of compressive sensing (CS) asserts that an unknown signal x in C^N canbe accurately recovered from m measurements with m << N provided that x is sparse. Most of the recovery algorithms need the sparsity s = ||x||_0 as an input. However,generally s is unknown, and directly estimating the sparsity has been an open problem.In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. Inthe simulation study and real data study, magnetic resonance image data is used asinput signal, which becomes sparse after sparsified transformation. The results fromthe simulation study confirm the theoretical properties of the estimator. In practice, theestimate from a real MR image can be used for recovering future MR images under theframework of CS if they are believed to have the same sparsity level after sparsification.

  • 74.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Department of Statistics, Zhejiang University City College, Hangzhou, China.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Enhanced block sparse signal recovery based on q-ratio block constrained minimal singular values2019Manuscript (preprint) (Other academic)
    Abstract [en]

    In this paper we introduce theq-ratio block constrained minimal singular values (BCMSV) as a new measure of measurement matrix in compressive sensing of block sparse/compressive signals and present an algorithm for computing this new measure. Both the mixed ℓ2/ℓq and the mixed ℓ2/ℓ1 norms of the reconstruction errors for stable and robust recovery using block Basis Pursuit (BBP), the block Dantzig selector (BDS) and the group lasso in terms of the q-ratio BCMSV are investigated. We establish a sufficient condition based on the q-ratio block sparsity for the exact recovery from the noise free BBP and developed a convex-concave procedure to solve the corresponding non-convex problem in the condition. Furthermore, we prove that for sub-Gaussian random matrices, theq-ratio BCMSV is bounded away from zero with high probability when the number of measurements is reasonably large. Numerical experiments are implemented to illustrate the theoretical results. In addition, we demonstrate that the q-ratio BCMSV based error bounds are tighter than the block restricted isotropic constant based bounds.

  • 75.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Error bounds of block sparse signal recovery based on q-ratio block constrained minimal singular values2019In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2019, article id 57Article in journal (Refereed)
    Abstract [en]

    In this paper, we introduce the q-ratio block constrained minimal singular values (BCMSV) as a new measure of measurement matrix in compressive sensing of block sparse/compressive signals and present an algorithm for computing this new measure. Both the mixed ℓ2/ℓq and the mixed ℓ2/ℓ1 norms of the reconstruction errors for stable and robust recovery using block basis pursuit (BBP), the block Dantzig selector (BDS), and the group lasso in terms of the q-ratio BCMSV are investigated. We establish a sufficient condition based on the q-ratio block sparsity for the exact recovery from the noise-free BBP and developed a convex-concave procedure to solve the corresponding non-convex problem in the condition. Furthermore, we prove that for sub-Gaussian random matrices, the q-ratio BCMSV is bounded away from zero with high probability when the number of measurements is reasonably large. Numerical experiments are implemented to illustrate the theoretical results. In addition, we demonstrate that the q-ratio BCMSV-based error bounds are tighter than the block-restricted isotropic constant-based bounds.

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  • 76.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Statistical inference for block sparsity of complex signals2019Manuscript (preprint) (Other academic)
    Abstract [en]

    Block sparsity is an important parameter in many algorithms to successfully recover block sparse signals under the framework of compressive sensing. However, it is often unknown and needs to beestimated. Recently there emerges a few research work about how to estimate block sparsity of real-valued signals, while there is, to the best of our knowledge, no investigation that has been conductedfor complex-valued signals. In this paper, we propose a new method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition,we demonstrate the importance of accurately estimating the block sparsity in signal recovery through asensitivity analysis.

  • 77.
    Wang, Jianfeng
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Statistical inference for block sparsity of complex-valued signals2020In: IET Signal Processing, ISSN 1751-9675, E-ISSN 1751-9683, Vol. 14, no 3, p. 154-161Article in journal (Refereed)
    Abstract [en]

    Block sparsity is an important parameter in many algorithms to successfully recover block-sparse signals under the framework of compressive sensing. However, it is often unknown and needs to be estimated. Recently there emerges a few research work about how to estimate block sparsity of real-valued signals, while there is, to the best of our knowledge, no research that has been done for complex-valued signals. In this study, we propose a method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition, we demonstrate the importance of accurately estimating the block sparsity through a sensitivity analysis.

  • 78.
    Wieloch, Thomas
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Ehlers, Ina
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Frank, David
    Grabner, Michael
    Gessler, Arthur
    Schleucher, Jürgen
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Intramolecular 13C analysis of tree rings provides multiple plant ecophysiology signals covering decades2018In: Scientific Reports, E-ISSN 2045-2322, Vol. 8, article id 5048Article in journal (Refereed)
    Abstract [en]

    Measurements of carbon isotope contents of plant organic matter provide important information in diverse fields such as plant breeding, ecophysiology, biogeochemistry and paleoclimatology. They are currently based on 13C/12C ratios of specific, whole metabolites, but we show here that intramolecular ratios provide higher resolution information. In the glucose units of tree-ring cellulose of 12 tree species, we detected large differences in 13C/12C ratios (>10‰) among carbon atoms, which provide isotopically distinct inputs to major global C pools, including wood and soil organic matter. Thus, considering position-specific differences can improve characterisation of soil-to-atmosphere carbon fluxes and soil metabolism. In a Pinus nigra tree-ring archive formed from 1961 to 1995, we found novel 13C signals, and show that intramolecular analysis enables more comprehensive and precise signal extraction from tree rings, and thus higher resolution reconstruction of plants’ responses to climate change. Moreover, we propose an ecophysiological mechanism for the introduction of a 13C signal, which links an environmental shift to the triggered metabolic shift and its intramolecular 13C signature. In conclusion, intramolecular 13C analyses can provide valuable new information about long-term metabolic dynamics for numerous applications.

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  • 79.
    Wieloch, Thomas
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Ehlers, Ina
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Frank, David
    Grabner, Michael
    Gessler, Arthur
    Schleucher, Jürgen
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Tree-ring cellulose exhibits several interannual 13C signals on the intramolecular level2018In: Geophysical Research Abstracts, 2018, Vol. 20, article id EGU2018-17509-2Conference paper (Refereed)
    Abstract [en]

    Measurements of carbon isotope contents (13C/12C, δ 13C) in tree rings provide retrospective information about the short and long-term dynamics of plant ecophysiological, and paleo-environmental traits. They are commonly based on 13C/12C ratios of cellulose, and interpreted with respect to fractionation related to CO2 diffusion into plants and its fixation by Rubisco (diffusion-Rubisco - DR - fractionation). However, primary metabolites such as glucose are known to exhibit intramolecular 13C/12C differences of the order of 10h which reflect 13C fractionation by enzyme reactions downstream of Rubisco (Post-Rubisco - PR - fractionation). PR fractionation is not commonly considered in dendrochronological studies. It has not yet been investigated whether glucose monomers of cellulose show intramolecular 13C differences. Furthermore, it is unknown whether PR fractionation varies among years, and whether DR and PR fractionations introduce distinct 13C/12C signals. To test this, we isolated the glucose monomers of Pinus nigra tree rings, and determined 13C/12C ratios of all intramolecular glucose carbon positions by quantitative 13C NMR. The resulting dataset consists of 6 time series of positional 13C/12C ratios with annual resolution, extending from 1961 to 1995. Tree-ring glucose exhibits intramolecular 13C/12C differences of the order of 10h. Cluster analysis revealed several independent intramolecular 13C signals. These signals constitute distinct channels of information about both the DR interface and associated environmental triggers, as well as PR processes related to downstream C allocation. Thus, analysis of intramolecular 13C signals can extract more information with better quality from tree rings. This might enhance our understanding of biogeochemical, ecophysiological and paleo-environmental phenomena.

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    Poster
  • 80.
    Wieloch, Thomas
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Grabner, Michael
    Institute of Wood Technology and Renewable Materials, University of Natural Resources and Life Sciences, Vienna, Austria.
    Augusti, Angela
    Research Institute on Terrestrial Ecosystems, National Research Council, Porano, Italy.
    Serk, Henrik
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Ehlers, Ina
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Schleucher, Jürgen
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Metabolism is a major driver of hydrogen isotope fractionation recorded in tree‐ring glucose of Pinus nigra2022In: New Phytologist, ISSN 0028-646X, E-ISSN 1469-8137, Vol. 234, no 2, p. 449-461Article in journal (Refereed)
    Abstract [en]
    • Stable isotope abundances convey valuable information about plant physiological processes and underlying environmental controls. Central gaps in our mechanistic understanding of hydrogen isotope abundances impede their widespread application within the plant and biogeosciences.
    • To address these gaps, we analysed intramolecular deuterium abundances in glucose of Pinus nigra extracted from an annually resolved tree-ring series (1961–1995).
    • We found fractionation signals (i.e. temporal variability in deuterium abundance) at glucose H1 and H2 introduced by closely related metabolic processes. Regression analysis indicates that these signals (and thus metabolism) respond to drought and atmospheric CO2 concentration beyond a response change point. They explain ≈ 60% of the whole-molecule deuterium variability. Altered metabolism is associated with below-average yet not exceptionally low growth.
    • We propose the signals are introduced at the leaf level by changes in sucrose-to-starch carbon partitioning and anaplerotic carbon flux into the Calvin–Benson cycle. In conclusion, metabolism can be the main driver of hydrogen isotope variation in plant glucose.
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  • 81.
    Wieloch, Thomas
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Grabner, Michael
    Augusti, Angela
    Serk, Henrik
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Ehlers, Ina
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Schleucher, Jürgen
    Umeå University, Faculty of Medicine, Department of Medical Biochemistry and Biophysics.
    Metabolism is the major driver of hydrogen isotope fractionation recorded in tree-ring glucose of Pinus nigra2021Manuscript (preprint) (Other academic)
    Abstract [en]

    - Stable isotope abundances convey valuable information about plant physiological processes and underlying environmental controls. Central gaps in our mechanistic understanding of hydrogen isotope abundances impede their widespread application within the plant and Earth sciences.

    - To close these gaps, we analysed intramolecular deuterium abundances in glucose of Pinus nigra extracted from an annually resolved tree-ring series (1961 to 1995).

    - We found fractionation signals at glucose H1 and H2 introduced by closely related metabolic processes. These signals (and thus metabolism) respond to drought and atmospheric CO2 concentration beyond a response change point. They explain ≈60% of the whole-molecule deuterium variability. Altered metabolism is associated with below-average yet not exceptionally low growth.

    - We propose the signals are introduced at the leaf-level by changes in sucrose-to-starch carbon partitioning and anaplerotic carbon flux into the Calvin-Benson cycle. In conclusion, metabolism can be the main driver of hydrogen isotope variation in plant glucose.

  • 82.
    Xie, Yingfu
    et al.
    SLU, Centre of Biostochastics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Asymptotics for Quasi-Maximum Likelihood Estimators of GARCH(1,2) Model Under Dependent Innovations2003Report (Other academic)
    Abstract [en]

    In this paper, we investigate the asymptotic properties of the quasi-maximum likelihood estimator (quasi-MLE) for GARCH(1,2) model under stationary innovations. Consistency of the global quasi-MLE and asymptotic normality of the local quasi-MLE are obtained, which extend the previous results for GARCH(1,1) under weaker conditions.

  • 83.
    Xie, Yingfu
    et al.
    Centre of Biostochastics, SLU.
    Yu, Jun
    Centre of Biostochastics, SLU.
    Consistency of Maximum Likelihood Estimators for the Reduced Regime-Switching GARCH Model2005Report (Other academic)
    Abstract [en]

    The regime-switching GARCH model combines the idea of Markov switching and GARCH model, which also extends Hidden Markov models. The statistical inference for this model, however, is rather difficult because the observations depend on the whole regime history. In this paper, we consider a reduced regime-switching GARCH model, that is, the past regimes are integrated out at every step and observations then depend only on the current regimes. We prove the consistency of maximum likelihood estimators for this model. Simulation studies to illustrate consistency, asymptotic normality of the proposed estimators and a model specification problem are also presented.

  • 84.
    Xie, Yingfu
    et al.
    SLU, Centre of Biostochastics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Consistency of Maximum Likelihood Estimators for the Reduced Regime-Switching GARCH Model2005Report (Other academic)
    Abstract [en]

    The regime-switching GARCH model combines the idea of Markov switching and GARCH model, which also extends Hidden Markov models. The statistical inference for this model, however, is rather difficult because the observations depend on the whole regime history. In this paper, we consider a reduced regime-switching GARCH model, that is, the past regimes are integrated out at every step and observations then depend only on the current regimes. We prove the consistency of maximum likelihood estimators for this model. Simulation studies to illustrate consistency, asymptotic normality of the proposed estimators and a model specification problem are also presented.

  • 85.
    Xie, Yingfu
    et al.
    SLU, Centre of Biostochastics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Ranneby, Bo
    SLU, Centre of Biostochastics.
    A General Autoregressive Modelwith Markov Switching: Estimation and Consistency2008In: Mathematical Methods of Statistics, ISSN 1066-5307, E-ISSN 1934-8045, Vol. 17, no 3, p. 228-240Article in journal (Refereed)
    Abstract [en]

    In this paper, a general autoregressive model with Markov switching is considered, where the autoregression may be of an infinite order. The consistency of the maximum likelihood estimators for this model is obtained under regularity assumptions. Examples of finite and infinite order autoregressive models with Markov switching are discussed. Simulation studies with these examples illustrate the consistency and asymptotic normality of the estimators.

  • 86.
    Xie, Yingfu
    et al.
    SLU, Centre of Biostochastics.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Ranneby, Bo
    SLU, Centre of Biostochastics.
    Forecasting using locally stationary wavelet processes2009In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 79, no 9, p. 1067-1082Article in journal (Refereed)
    Abstract [en]

    Locally stationary wavelet (LSW) processes, built on non-decimated wavelets, can be used to analyse and forecast non-stationary time series. They have been proved useful in the analysis of financial data. In this paper, we first carry out a sensitivity analysis, then propose some practical guidelines for choosing the wavelet bases for these processes. The existing forecasting algorithm is found to be vulnerable to outliers, and a new algorithm is proposed to overcome the weakness. The new algorithm is shown to be stable and outperforms the existing algorithm when applied to real financial data. The volatility forecasting ability of LSW modelling based on our new algorithm is then discussed and shown to be competitive with traditional GARCH models.

  • 87.
    Yu, Jun
    Hangzhou University, Department of Mathematics.
    Almost sure Lp-norm convergence for a k-nearest neighbor probability density estimate1987In: Journal of Hangzhou University, ISSN 0253-3618, Vol. 14, no 3, p. 278-284Article in journal (Refereed)
  • 88.
    Yu, Jun
    Hangzhou University, Department of Mathematics.
    Consistency of a k-nearest neighbor probability density function estimator1986In: Acta Mathematica Scientia, ISSN 0252-9602, E-ISSN 1003-3998, ISSN 1003-3998, Vol. 6, no 4, p. 467-477Article in journal (Refereed)
  • 89.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Consistency of a nearest neighbor density estimator for dependent variables2005In: Journal of nonparametric statistics (Print), ISSN 1048-5252, E-ISSN 1029-0311, Vol. 17, no 8, p. 873-884Article in journal (Refereed)
    Abstract [en]

    In this article, pointwise consistency and uniform complete consistency of an alternative nonparametric density estimator are proved for φ-mixing and α-mixing processes.

  • 90.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Mathematical statistics.
    Nearest neighbor probability density estimators1994Doctoral thesis, comprehensive summary (Other academic)
  • 91.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Mathematical statistics.
    Nearest-Neighbour Density Estimation1998In: Encyclopedia of Statistical Sciences: Update Volume 2 / [ed] Samuel Kotz, Campbell B. Read and David L. Banks, New York: John Wiley & Sons, 1998, p. 461-467Chapter in book (Refereed)
  • 92.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Spatiotemporal modelling in MRI measurements for cancer therapy assessment2016Conference paper (Other academic)
  • 93.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Statistical modelling for spatiotemporal data2019Conference paper (Other academic)
  • 94.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Mathematical statistics.
    Uniform convergence rates for a nearest neighbor density estimator under dependence assumptions1997In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 26, no 3, p. 601-616Article in journal (Refereed)
    Abstract [en]

    In this paper the rates of strong uniform convergence over any compact set for an alternative nearest neighbor density estimator are obtained when the observations satisfy a ø-mixing or an a-mixing condition. In the ø-mixing case we obtain a quite better convergence rate than for a-mixing processes and we do not require a geometric condition on the mixing coefficients. For independent or m-dependent observations, as a special case of ømixing, the result gives us the optimal rate of strong uniform convergence for density estimators.

  • 95.
    Yu, Jun
    Biostokastikum, SLU.
    Wavelet based noise reduction and parameter estimation in magnetic resonance signals2010In: Smögen workshop 2010: Titles and abstracts, 2010Conference paper (Refereed)
    Abstract [en]

    The project deals with development of biostochastic models for analysis of spatial-temporal signals with applications in magnetic resonance. We developed a noise reduction algorithm for 4D MRI signals, based the wavelet transform and Gaussian scale mixtures. We also derived the maximum likelihood estimators for the parameters of a modified Rice distribution, which arises from the MR signals. Asymptotic properties of the estimators were obtainedand an ECM-algorithm for simultaneous estimation was developed.

  • 96.
    Yu, Jun
    et al.
    Umeå University, Faculty of Science and Technology, Mathematical statistics.
    Ekström, Magnus
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Asymptotic properties of high order spacings under dependence assumptions2000In: Mathematical Methods of Statistics, ISSN 1066-5307, E-ISSN 1934-8045, Vol. 9, no 4, p. 437-448Article in journal (Refereed)
    Abstract [en]

    Strong limit theorems are proved for sums of logarithms of spacings of increasing order when the observations satisfy a phi-mixing or an alpha-mixing condition. Applications of the results in goodness of fit and parametric estimation problems are discussed.

  • 97.
    Yu, Jun
    et al.
    Centre of Biostochastics, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Ekström, Magnus
    Centre of Biostochastics, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Multispectral image classification using wavelets: a simulation study2003In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 36, no 4, p. 889-898Article in journal (Refereed)
    Abstract [en]

    This work presents methods for multispectral image classification using the discrete wavelet transform. Performance of some conventional classification methods is evaluated, through a Monte Carlo study, with or without using the wavelet transform. Spatial autocorrelation is present in the computer-generated data on different scenes, and the misclassification rates are compared. The results indicate that the wavelet-based method performs best among the methods under study.

  • 98.
    Yu, Jun
    et al.
    SLU, Department of Forest Resource Management and Geomatics.
    Ekström, Magnus
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Nilsson, Mats
    SLU, Department of Forest Resource Management and Geomatics.
    Image Classication using Wavelets with Application to Forestry2000In: Proceedings of the 5th International Conference on Methodological Issues in Official Statistics, 2000Conference paper (Refereed)
    Abstract [en]

    This work presents methods for image classication using the discrete wavelet transform. Performance of some conventional classication methods is evaluated, throughboth a Monte Carlo study and a real Landsat TM image together with the National Forest Inventory (NFI) eld data, with or without using the wavelet transform. In our evaluation on the real data, the bootstrap is applied to estimate classication errors. The results indicate that the wavelet based method performs best among the methods under study.

  • 99.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Englund, Göran
    Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
    Predator-Prey Covariance with predator aggregative responses2010Report (Other academic)
    Abstract [en]

    The spatial covariance between prey and predator densities is closely related to the rate of encounters, and thus to predation rates. To include the effect of covariance in dynamic predator–prey models it is useful to express the spatial covariance as a function of predator and prey densities. Here we derive mean–covariance relationships for a scenario where predators show an aggregative response, i.e., they respond behaviorally by aggregating in patches with high prey densities. Prey, on the otherhand, do not respond to predator densities. Some explicit expressions are obtained when the prey distribution is clumped or random. It is shown that the prey-predator covariance can be expressed only through the distributional information of prey. In particular when the prey distributionis clumped or random, this covariance depends only on the mean prey density.

  • 100.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Karlsson, Stefan
    Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Local spectral analysis using wavelet packets2001In: Circuits, systems, and signal processing, ISSN 0278-081X, E-ISSN 1531-5878, Vol. 20, no 5, p. 497-528Article in journal (Refereed)
    Abstract [en]

    Wavelet packets are a useful extension of wavelets, which are of wide potential use in a statistical context. In this paper, an approach to the local spectral analysis of a stationary time series based on wavelet packet decomposition is developed. This involves extensions to the wavelet context of standard time series ideas such as the periodogram and spectrum. Some asymptotic properties of the new estimate are provided. The technique is illustrated by simulated signals and its application to physiological data, and its potential use in studies of time-dependent spectral analysis is discussed.

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