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  • 51.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Garpebring, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Brynolfsson, Patrik
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using Bayesian hierarchical model2016Ingår i: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling, 2016, s. 217-217Konferensbidrag (Övrigt vetenskapligt)
  • 52.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Sparsity Estimation of MR images in Compressive Sensing2017Konferensbidrag (Övrigt vetenskapligt)
  • 53.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Zhou, Zhiyong
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Garpebring, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Bayesian sparsity estimation in compressive sensing with application to MR images2019Ingår i: Communications in Statistics: Case Studies, Data Analysis and Applications, ISSN 2373-7484, Vol. 5, nr 4, s. 415-431Artikel i tidskrift (Refereegranskat)
    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.

  • 54.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Zhou, Zhiyong
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Garpebring, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Sparsity estimation in compressive sensing with application to MR images2017Manuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 55.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Zhou, Zhiyong
    Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Error bounds of block sparse signal recovery based on q-ratio block constrained minimal singular values2019Ingår i: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2019, artikel-id 57Artikel i tidskrift (Refereegranskat)
    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.

  • 56.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Zhou, Zhiyong
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Statistical inference for block sparsity of complex signals2019Manuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 57.
    Wang, Jianfeng
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Zhou, Zhiyong
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Statistical inference for block sparsity of complex-valued signals2020Ingår i: IET Signal Processing, ISSN 1751-9675, E-ISSN 1751-9683Artikel i tidskrift (Refereegranskat)
    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.

  • 58.
    Wieloch, Thomas
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Ehlers, Ina
    Frank, David
    Gessler, Arthur
    Grabner, Michael
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Schleucher, Jürgen
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Tree-ring cellulose exhibits several distinct intramolecular 13C signals2017Ingår i: Geophysical Research Abstracts, 2017, Vol. 19, artikel-id EGU2017-14723Konferensbidrag (Refereegranskat)
  • 59.
    Wieloch, Thomas
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Ehlers, Ina
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Frank, David
    Grabner, Michael
    Gessler, Arthur
    Schleucher, Jürgen
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Intramolecular 13C analysis of tree rings provides multiple plant ecophysiology signals covering decades2018Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, artikel-id 5048Artikel i tidskrift (Refereegranskat)
    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.

  • 60.
    Wieloch, Thomas
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Ehlers, Ina
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Frank, David
    Grabner, Michael
    Gessler, Arthur
    Schleucher, Jürgen
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Tree-ring cellulose exhibits several interannual 13C signals on the intramolecular level2018Ingår i: Geophysical Research Abstracts, 2018, Vol. 20, artikel-id EGU2018-17509-2Konferensbidrag (Refereegranskat)
    Abstract
  • 61.
    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 Innovations2003Rapport (Övrigt vetenskapligt)
    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.

  • 62.
    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 Model2005Rapport (Övrigt vetenskapligt)
    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.

  • 63.
    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 Model2005Rapport (Övrigt vetenskapligt)
    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.

  • 64.
    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 Consistency2008Ingår i: Mathematical Methods of Statistics, ISSN 1066-5307, E-ISSN 1934-8045, Vol. 17, nr 3, s. 228-240Artikel i tidskrift (Refereegranskat)
    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.

  • 65.
    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 processes2009Ingår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 79, nr 9, s. 1067-1082Artikel i tidskrift (Refereegranskat)
    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.

  • 66.
    Yu, Jun
    Hangzhou University, Department of Mathematics.
    Almost sure Lp-norm convergence for a k-nearest neighbor probability density estimate1987Ingår i: Journal of Hangzhou University, ISSN 0253-3618, Vol. 14, nr 3, s. 278-284Artikel i tidskrift (Refereegranskat)
  • 67.
    Yu, Jun
    Hangzhou University, Department of Mathematics.
    Consistency of a k-nearest neighbor probability density function estimator1986Ingår i: Acta Mathematica Scientia, ISSN 0252-9602, E-ISSN 1003-3998, ISSN 1003-3998, Vol. 6, nr 4, s. 467-477Artikel i tidskrift (Refereegranskat)
  • 68.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Consistency of a nearest neighbor density estimator for dependent variables2005Ingår i: Journal of nonparametric statistics (Print), ISSN 1048-5252, E-ISSN 1029-0311, Vol. 17, nr 8, s. 873-884Artikel i tidskrift (Refereegranskat)
    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.

  • 69.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Matematisk statistik.
    Nearest neighbor probability density estimators1994Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
  • 70.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Matematisk statistik.
    Nearest-Neighbour Density Estimation1998Ingår i: Encyclopedia of Statistical Sciences: Update Volume 2 / [ed] Samuel Kotz, Campbell B. Read and David L. Banks, New York: John Wiley & Sons, 1998, s. 461-467Kapitel i bok, del av antologi (Refereegranskat)
  • 71.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Spatiotemporal modelling in MRI measurements for cancer therapy assessment2016Konferensbidrag (Refereegranskat)
  • 72.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Matematisk statistik.
    Uniform convergence rates for a nearest neighbor density estimator under dependence assumptions1997Ingår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 26, nr 3, s. 601-616Artikel i tidskrift (Refereegranskat)
    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.

  • 73.
    Yu, Jun
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Matematisk statistik.
    Ekström, Magnus
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Asymptotic properties of high order spacings under dependence assumptions2000Ingår i: Mathematical Methods of Statistics, ISSN 1066-5307, E-ISSN 1934-8045, Vol. 9, nr 4, s. 437-448Artikel i tidskrift (Refereegranskat)
    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.

  • 74.
    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 study2003Ingår i: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 36, nr 4, s. 889-898Artikel i tidskrift (Refereegranskat)
    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.

  • 75.
    Yu, Jun
    et al.
    SLU, Department of Forest Resource Management and Geomatics.
    Ekström, Magnus
    Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.
    Nilsson, Mats
    SLU, Department of Forest Resource Management and Geomatics.
    Image Classication using Wavelets with Application to Forestry2000Ingår i: Proceedings of the 5th International Conference on Methodological Issues in Official Statistics, 2000Konferensbidrag (Refereegranskat)
    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.

  • 76.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Englund, Göran
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Predator-Prey Covariance with predator aggregative responses2010Rapport (Övrigt vetenskapligt)
    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.

  • 77.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Karlsson, Stefan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Centrum för medicinsk teknik och fysik (CMTF).
    Local spectral analysis using wavelet packets2001Ingår i: Circuits, systems, and signal processing, ISSN 0278-081X, E-ISSN 1531-5878, Vol. 20, nr 5, s. 497-528Artikel i tidskrift (Refereegranskat)
    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.

  • 78.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Leonardsson, Kjell
    SLU, Department of Wildlife, Fish, and Environmental Studies.
    Distribution Estimation for Fishing Time2011Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Information on visitation frequencies in recreational fishing is important when dealing with fishing tourism in order to make prognoses forthe future upon changes in the management of the aquatic environment (flow regime or habitat restoration), fishing regulations, or to estimatethe total harvest of fish. Therefore interviews have been performed in a number of streams and sections of streams, throughout the last twenty years in the Jämtland-Härjedalen region in Sweden. In this work the probability distribution of total fishing hours a day (possibly on differentperiods) is considered. We found that both Gamma and Weibull distributions can be considered as approximate distributions that generate the data. Gamma distribution fits very well for summer season while Weibull distribution is more appropriate for the other periods. In general, the gamma model is easier to interpret and better fits the mode of the distribution, and therefore, is preferred. Having parameters estimated, we are able to calculate probabilities of different fishing times. It is also suggested to use two periods: mid-summer – the end of August and other dates. The modelling at section level seems to be successful. Both the Gamma and Weibull distributions fits well the data for all periods, providing that the number of observations are not less than 20. The mean fishing hours, however, varies from section to section, even within the same watercourse.

  • 79.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Ranneby, Bo
    SLU, Centre of Biostochastics.
    Classification of agricultural crops and quality assessmentusing multispectral and multitemporal images2005Ingår i: Proceedings of The 9th International Symposium on Physical Measurements and Signature in Remote Sensing, 2005, s. 692-694Konferensbidrag (Refereegranskat)
  • 80.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Ranneby, Bo
    SLU, Centre of Biostochastics.
    Nonparametric and probabilistic classification of agricultural crops using multitemporal images2007Ingår i: Journal of Remote Sensing, ISSN 1007-4619, Vol. 11, nr 5, s. 748-755Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, a new approach for classification of multitemporal satellite data sets, combining multispectral and change detection techniques is proposed. The algorithm is based on the nearest neighbor method and derived in order to optimize the average probability for correct classification, i.e. each class is equally important. The new algorithm was applied to a study area where satellite images (SPOT and Landsat TM) from different seasons were used. It showed that using five seasonal images can substantially improve the classification accuracy compared to using a single image. As a large scale application, the approach was applied to the River Dalälven drainage basin.

    As the distributions for different classes are highly overlapping it is not possible to get satisfactory accuracy at pixel level. Instead it is necessary to introduce a new concept, pixel-wise probabilistic classifiers. The pixel-wise vectors of probabilities can be used to judge how reliable a traditional classification is and to derive measures of the uncertainty (entropy) for the individual pixels. The probabilistic classifier gives also unbiased area estimates overarbitrary areas. It has been tested on two test sites of arable land with different characteristics.

  • 81.
    Yu, Jun
    et al.
    Biostokastikum, SLU.
    Ranneby, Bo
    Biostokastikum, SLU.
    Nonparametric and probabilistic classification of agricultural crops using multitemporal images2006Konferensbidrag (Refereegranskat)
  • 82.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Ranneby, Bo
    SLU, Centre of Biostochastics.
    Nonparametric classification and probabilistic classifiers with environmental and remote sensing applications2007Ingår i: Asymptotic Theory in Probability and Statistics with Applications / [ed] Tze Leung Lai, Qi-Man Shao, Lianfen Qian, Beijing: Higher Education Press, 2007, s. 388-436Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    National and international policies today require environmental monitoring and follow-up systems that detect, in a quality assured way, changes over time in land use and landscape indicators. Remote sensing of satellite images offers great potential to assess wall-to-wall changes in the health of ecosystems and identify risks. 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 and spatial indicators using multispectral and multitemporal satellite images. They are developed under non-standard conditions - conditions under which many statistical methods do not work properly but frequently appear in environmental and remote sensing applications. Error rates of traditional remote sensing classification methods are usually quite high but can be improved by (1) denoising the images using the wavelet transform, (2) reclassification using Markov random field approaches, or (3) applying new classification algorithms based on nonparametric classifiers. In order to assess quality of classification at pixel level, a new concept, the probabilistic classifiers, is introduced. These classifiers are useful for measuring uncertainty at pixel level and obtaining reliable area estimates locally. Results from both simulation studies and real applications are presented.

  • 83.
    Yu, Jun
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Zhou, Zhiyong
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Stable and robust ℓp-constrained compressive sensing recovery via robust width property2019Ingår i: Journal of the Korean Mathematical Society, ISSN 0304-9914, E-ISSN 2234-3008, Vol. 56, nr 3, s. 689-701Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We study the recovery results of ℓp-constrained compressive sensing (CS) with p≥1 via robust width property and determine conditions on the number of measurements for standard Gaussian matrices under which the property holds with high probability. Our paper extendsthe existing results in Cahill and Mixon [2] from ℓ2-constrained CS to ℓp-constrained case with p≥1 and complements the recovery analysisfor robust CS with ℓp loss function.

  • 84.
    Yu, Jun
    et al.
    SLU, Centre of Biostochastics.
    Östlund, Nils
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Centrum för medicinsk teknik och fysik (CMTF). Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
    Wavelet based noise reduction of four-dimensional data with application to MRI2012Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This paper is devoted to the development of noise reduction methods for spatial-temporal signals with applications in magnetic resonance imaging. A noise reduction algorithm for 4D MRI signals, based on thewavelet transform and Gaussian scale mixtures, is proposed here. Simulation study shows that the new method is capable to improve theperformance of noise reduction in higher dimensions.

  • 85.
    Yu, Jun
    et al.
    Biostokastikum, SLU.
    Östlund, Nils
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Centrum för medicinsk teknik och fysik (CMTF). Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
    Löthgren, Pia
    Biostokastikum, SLU.
    Wavelet based noise reduction and parameter estimation in magnetic resonance signals2010Konferensbidrag (Refereegranskat)
  • 86.
    Zhang, Huichun
    et al.
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
    Dorr, Gary
    Faculty of Science, The University of Queensland, Brisbane 4343, Australia.
    Zhang, Jiaqiang
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
    Zhou, Hongping
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Wind tunnel experiment and regression model for spray drift2015Ingår i: Transactions of the Chinese Society of Agricultural Engineering, ISSN 1002-6819, Vol. 31, nr 3, s. 94-100Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With greater environmental awareness, the movement of pesticides within and off of a spray target area is acritical public concern. Ideally, all of the material applied should be deposited within the targeted swath on the intendedpest or plant. But realistically, a portion of the spray remains airborne and is carried downwind to non-target areas.Airborne spray leaving the targeted area reduces the applied dosage, and could cause damage to neighboring plant andwater source or other detrimental environmental impacts. To study the influences of nozzle type, spray mixture and windspeed on spray drift, experiments were conducted using a wind tunnel. Spray drift risk was assessed by adding a tracer tothe spray mixture and measuring the quantities of spray deposited downwind from the nozzle on horizontal polythenelines with 2 mm diameter perpendicular to the wind direction in a vertical and a horizontal array. At a distance of 2 mdownwind from the static nozzle, five collector lines (V1 to V5) were positioned one above the other at the spacing of0.1 m to provide an estimate of the spray still airborne through this vertical profile. An additional five sampling collectorstrings (H1 to H5) were placed in a horizontal array with one-meter horizontal spacing at 0.1 m height to determine thefallout volumes and gradients of the spray from 2 to 6 m downwind. A water-soluble fluorescent tracer was dissolvedinto tap water as the spray liquid, and after the experiments, the collecting lines were washed with deionized water tomeasure deposit and drift. The results indicated that deposits on sampling collector decreased with increased verticalelevation and horizontal distance. Average fallout and airborne deposit resulting from the different spray applicationswere shown in the paper. These figures showed the expected fallout and airborne profiles for all tested nozzle types andsizes. The highest fallout deposits were measured at a position closest to the nozzle (H1) with a systematic decrease withthe distance from the nozzle. The highest airborne deposits were found at the lowest sampling collector (V1) with asystematic decrease with increasing height above the wind tunnel floor. Airborne spray drift was affected by wind speed.At all sample positions, deposits on collectors were reduced at lower wind speed. Nozzle’s structure was also found toinfluence droplet’s size, so injector/pre-orifice nozzle produced coarser droplets and reduced spray drift. The amount ofspray recovered is based on the amount of active ingredient of spray mixture within each droplet rather than the totaldroplet volume. On that basis, a multiple non-linear model for statistical drift prediction including four independent,non-correlated variables (target distance, wind speed, nozzle type and chemical type) was established. The regressionmodel provided a drift evaluation approach, and it was important in the interpretation of wind tunnel data for differentnozzle types, chemical types and sampling methodologies.

  • 87.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    A New Nonconvex Sparse Recovery Method for Compressive Sensing2019Ingår i: Frontiers in Applied Mathematics and Statistics, ISSN 2297-4687, Vol. 5, s. 1-11, artikel-id 14Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As an extension of the widely used ℓr-minimization with 0 < r ≤ 1, a new non-convex weighted ℓr − ℓ1 minimization method is proposed for compressive sensing. The theoretical recovery results based on restricted isometry property and q-ratio constrained minimal singular values are established. An algorithm that integrates the iteratively reweighted least squares algorithm and the difference of convex functions algorithmis given to approximately solve this non-convex problem. Numerical experiments are presented to illustrate our results.

  • 88.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Adaptive estimation for varying coefficient modelswith nonstationary covariates2019Ingår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 48, nr 16, s. 4034-4050Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, the adaptive estimation for varying coefficient models proposed by Chen, Wang, and Yao (2015) is extended to allowing for nonstationary covariates. The asymptotic properties of the estimator are obtained, showing different convergence rates for the integrated covariates and stationary covariates. The nonparametric estimator of the functional coefficient with integrated covariates has a faster convergence rate than the estimator with stationary covariates, and its asymptotic distribution is mixed normal. Moreover, the adaptive estimation is more efficient than the least square estimation for non normal errors. A simulation study is conducted to illustrate our theoretical results.

  • 89.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Estimation of block sparsity in compressive sensing2017Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    In this paper, we consider a soft measure of block sparsity, k_α(x)=(∥x∥2,α/∥x∥2,1)^α/(1−α),α∈[0,∞] and propose a procedure to estimate it by using multivariate isotropic symmetric α-stable random projections without sparsity or block sparsity assumptions. The limiting distribution of the estimator is given. Some simulations are conducted to illustrate our theoretical results.

  • 90.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Estimation of block sparsity in compressive sensing2017Konferensbidrag (Refereegranskat)
  • 91. Zhou, Zhiyong
    et al.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    On q-ratio CMSV for sparse recovery2019Ingår i: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 165, s. 128-132Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As a kind of computable incoherence measure of the measurement matrix, q-ratio constrained minimal singular values (CMSV) was proposed in Zhou and Yu (2019) to derive the performance bounds for sparse recovery. In this paper, we study the geometrical properties of the q-ratio CMSV, based on which we establish new sufficient conditions for signal recovery involving both sparsity defect and measurement error. The ℓ1-truncated set q-width of the measurement matrix is developed as the geometrical characterization of q-ratio CMSV. In addition, we show that the q-ratio CMSVs of a class of structured random matrices are bounded away from zero with high probability as long as the number of measurements is large enough, therefore these structured random matrices satisfy those established sufficient conditions. Overall, our results generalize the results in Zhang and Cheng (2012) from q=2 to any q ∈ (1, ∞] and complement the arguments of q-ratio CMSV from a geometrical view.

  • 92.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Phaseless compressive sensing using partial support information2017Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    We study the recovery conditions of weighted l_1 minimization for real signal reconstruction fromphaseless compressed sensing measurements when partial support information is available. A Strong Restricted Isometry Property (SRIP) condition is provided to ensure the stable recovery. Moreover,we present the weighted null space property as the sucient and necessary condition for the success of k-sparse phase retrieval via weighted l_1 minimization

  • 93.
    Zhou, Zhiyong
    et al.
    Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Phaseless compressive sensing using partial support information2019Ingår i: Optimization Letters, ISSN 1862-4472, E-ISSN 1862-4480Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We study the recovery conditions of weighted ℓ1 minimization for real-valued signal reconstruction from phaseless compressive sensing measurements when partial support information is available. A strong restricted isometry property condition is provided to ensure the stable recovery. Moreover, we present the weighted null space property as the sufficient and necessary condition for the success of k-sparse phaseless recovery via weighted ℓ1 minimization. Numerical experiments are conducted to illustrate our results.

  • 94.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Recovery analysis for weighted mixed ℓ2/ℓp minimization with 0 < p ≤ 12019Ingår i: Journal of Computational and Applied Mathematics, ISSN 0377-0427, E-ISSN 1879-1778, Vol. 352, s. 12s. 210-222Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We study the recovery conditions of weighted mixed ℓ2/ℓp (0 < p ≤ 1) minimization for block sparse signal reconstruction from compressed measurements when partial block support information is available. We show that the block p-restricted isometry property (RIP) can ensure the robust recovery. Moreover, we present the sufficient and necessary condition for the recovery by using weighted block p-null space property. The relationship between the block p-RIP and the weighted block p-null space property has been established. Finally, we illustrate our results with a series of numerical experiments.

  • 95.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Sparse recovery based on q-ratio constrained minimal singular values2018Manuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    We study verifiable sufficient conditions and computable performance bounds for sparse recovery algorithms such as the Basis Pursuit, the Dantzig selector and the Lasso estimator, in terms of a newly defined family of quality measures for the measurement matrices. With high probability, the developed measures for subgaussian random matrices are bounded away from zero as long as the number of measurements is reasonably large. Comparing to the restricted isotropic constant based performance analysis, the arguments in this paper are much more concise and the obtained bounds are tighter. Numerical experiments are presented to illustrate our theoretical results.

  • 96.
    Zhou, Zhiyong
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Yu, Jun
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
    Sparse recovery based on q-ratio constrained minimal singular values2019Ingår i: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 155, s. 247-258Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We study verifiable sufficient conditions and computable performance bounds for sparse recovery algorithms such as the Basis Pursuit, the Dantzig selector and the Lasso estimator, in terms of a newly defined family of quality measures for the measurement matrices. With high probability, the developed measures for subgaussian random matrices are bounded away from zero as long as the number of measurements is reasonably large. Comparing to the restricted isotropic constant based performance analysis, the arguments in this paper are much more concise and the obtained bounds are tighter. Numerical experiments are presented to illustrate our theoretical results.

  • 97.
    Åkerblom, Staffan
    et al.
    SLU, Department of Aquatic Sciences and Assessment.
    Nilsson, Mats
    SLU, Department of Forest Ecology and Management.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Ranneby, Bo
    SLU, Centre of Biostochastics.
    Johansson, Kjell
    Temporal change estimation of mercury concentrations in northern pike (Esox lucius L.) in Swedish lakes2012Ingår i: Chemosphere, ISSN 0045-6535, E-ISSN 1879-1298, Vol. 86, nr 5, s. 439-445Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Adequate temporal trend analysis of mercury (Hg) in freshwater ecosystems is critical to evaluate if actions from the human society have affected Hg concentrations ([Hg]) in fresh water biota. This study examined temporal change in [Hg] in Northern pike (Esox lucius L.) in Swedish freshwater lakes between 1994 and 2006. To achieve this were lake-specific, multiple-linear-regression models used to estimate pike [Hg], including indicator variables representing time and fish weight and their interactions. This approach permitted estimation of the direction and magnitude of temporal changes in 25 lakes selected from the Swedish national database on Hg in freshwater biota. A significant increase was found in 36% of the studied lakes with an average increase in pike [Hg] of 3.7 +/- 6.7% per year that was found to be positively correlated with total organic carbon. For lakes with a significant temporal change the dataset was based on a mean of 30 fish, while for lakes with no temporal change it was based on a mean of 13 fish.

  • 98.
    Östlund, Nils
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Centrum för medicinsk teknik och fysik (CMTF).
    Wiklund, Urban
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Centrum för medicinsk teknik och fysik (CMTF).
    Yu, Jun
    Centre of Biostochastics, SLU.
    Karlsson, Stefan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Centrum för medicinsk teknik och fysik (CMTF).
    Adaptive spatio-temporal filtration of bioelectrical signals2005Ingår i: Proceedings of The 27th Annual International Conferenceof the IEEE Engineering in Medicine and Biology Society, New York: IEEE Press, 2005, s. 5983-5986Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we show how independent component analysis (ICA) algorithms can be used to perform spatio-temporal filtration of electromyographic (EMG) and electrocardiographic (ECG) signals. The technique was used to decompose the EMG signals into motor unit action potential (MUAP) trains. From the 88 outputs of the adaptive spatio-temporal filtration, three groups of different MUAP train patterns were found. The technique was also used to obtain a fetus' ECG and showed better result compared to using ICA.

  • 99.
    Östlund, Nils
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Karlsson, Stefan
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
    Adaptive spatio-temporal filtering of multichannel surface EMG signals2006Ingår i: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 44, nr 3, s. 209-215Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A motor unit (MU) is defined as an anterior horn cell, its axon, and the muscle fibres innervated by the motor neuron. A surface electromyogram (EMG) is a superposition of many different MU action potentials (MUAPs) generated by active MUs. The objectives of this study were to introduce a new adaptive spatio-temporal filter, here called maximum kurtosis filter (MKF), and to compare it with existing filters, on its performance to detect a single MUAP train from multichannel surface EMG signals. The MKF adaptively chooses the filter coefficients by maximising the kurtosis of the output. The proposed method was compared with five commonly used spatial filters, the weighted low-pass differential filter (WLPD) and the marginal distribution of a continuous wavelet transform. The performance was evaluated using simulated EMG signals. In addition, results from a multichannel surface EMG measurement fro from a subject who had been previously exposed to radiation due to cancer were used to demonstrate an application of the method. With five time lags of the MKF, the sensitivity was 98.7% and the highest sensitivity of the traditional filters was 86.8%, which was obtained with the WLPD. The positive predictivities of these filters were 87.4 and 80.4%, respectively. Results from simulations showed that the proposed spatio-temporal filtration technique significantly improved performance as compared with existing filters, and the sensitivity and the positive predictivity increased with an increase in number of time lags in the filter.

  • 100.
    Östlund, Nils
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Yu, Jun
    SLU, Centre of Biostochastics.
    Karlsson, Stefan
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Improved maximum frequency estimation with application to instantaneous mean frequency estimation of surface electromyography2004Ingår i: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 51, nr 9, s. 1541-1546Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The purpose of this study was to improve the maximum-frequency estimation. Three methods to estimate the maximum frequency of a bandlimited signal with additive white noise were compared. Two existing methods, the threshold-crossing method (TCM) and the hybrid method, were modified for time-frequency representations. A novel approach, the running-block threshold method (RBTM), was introduced. Based on calculation of detection probability (sensitivity) the RBTM improved the maximum-frequency estimate as compared with the TCM. The maximum-frequency estimation methods were also used to determine the integration interval for instantaneous mean-frequency (IMNF) estimation from synthesized surface electromyography containing white noise. Results showed that the IMNF estimate was improved by using any of the three methods and that the RBTM gave the best IMNF estimate.

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