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BETA
Cronie, Ottmar
Publications (10 of 15) Show all publications
Bayisa, F., Zhou, Z., Cronie, O. & Yu, J. (2019). Adaptive algorithm for sparse signal recovery. Digital signal processing (Print), 87, 10-18
Open this publication in new window or tab >>Adaptive algorithm for sparse signal recovery
2019 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 87, p. 16p. 10-18Article in journal (Refereed) Published
Abstract [en]

The development of compressive sensing in recent years has given much attention to sparse signal recovery. In sparse signal recovery, spike and slab priors are playing a key role in inducing sparsity. The use of such priors, however, results in non-convex and mixed integer programming problems. Most of the existing algorithms to solve non-convex and mixed integer programming problems involve either simplifying assumptions, relaxations or high computational expenses. In this paper, we propose a new adaptive alternating direction method of multipliers (AADMM) algorithm to directly solve the suggested non-convex and mixed integer programming problem. The algorithm is based on the one-to-one mapping property of the support and non-zero element of the signal. At each step of the algorithm, we update the support by either adding an index to it or removing an index from it and use the alternating direction method of multipliers to recover the signal corresponding to the updated support. Moreover, as opposed to the competing “adaptive sparsity matching pursuit” and “alternating direction method of multipliers” methods our algorithm can solve non-convex problems directly. Experiments on synthetic data and real-world images demonstrated that the proposed AADMM algorithm provides superior performance and is computationally cheaper than the recently developed iterative convex refinement (ICR) and adaptive matching pursuit (AMP) algorithms.

Place, publisher, year, edition, pages
Elsevier, 2019. p. 16
Keywords
sparsity, adaptive algorithm, sparse signal recovery, spike and slab priors
National Category
Probability Theory and Statistics Signal Processing Medical Image Processing
Research subject
Mathematical Statistics; Signal Processing
Identifiers
urn:nbn:se:umu:diva-146386 (URN)10.1016/j.dsp.2019.01.002 (DOI)000461266700002 ()2-s2.0-85060542792 (Scopus ID)
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-534
Note

Originally included in thesis in manuscript form

Available from: 2018-04-07 Created: 2018-04-07 Last updated: 2019-04-04Bibliographically approved
Toreti, A., Cronie, O. & Zampieri, M. (2019). Concurrent climate extremes in the key wheat producing regions of the world. Scientific Reports, 9, Article ID 5493.
Open this publication in new window or tab >>Concurrent climate extremes in the key wheat producing regions of the world
2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 5493Article in journal (Refereed) Published
Abstract [en]

Climate extremes have profound impacts on key socio-economic sectors such as agriculture. In a changing climate context, characterised by an intensification of these extremes and where the population is expected to grow, exposure and vulnerability must be accurately assessed. However, most risk assessments analyse extremes independently, thus potentially being overconfident in the resilience of the socio-economic sectors. Here, we propose a novel approach to defining and characterising concurrent climate extremes (i.e. extremes occurring within a specific temporal lag), which is able toidentify spatio-temporal dependences without making any strict assumptions. the method is applied to large-scale heat stress and drought events in the key wheat producing regions of the world, as these extremes can cause serious yield losses and thus trigger market shocks. Wheat regions likely to haveconcurrent extremes (heat stress and drought events) are identified, as well as regions independent ofeach other or inhibiting each other in terms of these extreme events. this tool may be integrated in all risk assessments but could also be used to explore global climate teleconnections.

Place, publisher, year, edition, pages
Nature Publishing Group, 2019
National Category
Climate Research
Identifiers
urn:nbn:se:umu:diva-152074 (URN)10.1038/s41598-019-41932-5 (DOI)000462990000035 ()30940858 (PubMedID)
Available from: 2018-09-25 Created: 2018-09-25 Last updated: 2019-05-23Bibliographically approved
Moradi, M. M., Cronie, O., Rubak, E., Lachieze-Rey, R., Mateu, J. & Baddeley, A. (2019). Resample-smoothing of Voronoi intensity estimators. Statistics and computing, 29(5), 995-1010
Open this publication in new window or tab >>Resample-smoothing of Voronoi intensity estimators
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2019 (English)In: Statistics and computing, ISSN 0960-3174, E-ISSN 1573-1375, Vol. 29, no 5, p. 995-1010Article in journal (Refereed) Epub ahead of print
Abstract [en]

Voronoi estimators are non-parametric and adaptive estimators of the intensity of a point process. The intensity estimate at a given location is equal to the reciprocal of the size of the Voronoi/Dirichlet cell containing that location. Their major drawback is that they tend to paradoxically under-smooth the data in regions where the point density of the observed point pattern is high, and over-smooth where the point density is low. To remedy this behaviour, we propose to apply an additional smoothing operation to the Voronoi estimator, based on resampling the point pattern by independent random thinning. Through a simulation study we show that our resample-smoothing technique improves the estimation substantially. In addition, we study statistical properties such as unbiasedness and variance, and propose a rule-of-thumb and a data-driven cross-validation approach to choose the amount of smoothing to apply. Finally we apply our proposed intensity estimation scheme to two datasets: locations of pine saplings (planar point pattern) and motor vehicle traffic accidents (linear network point pattern).

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Adaptive intensity estimation, Complete separable metric space, Independent thinning, Point process, Resampling, Voronoi intensity estimator
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics; Mathematics; Statistics
Identifiers
urn:nbn:se:umu:diva-152073 (URN)10.1007/s11222-018-09850-0 (DOI)000482225200009 ()2-s2.0-85060327193 (Scopus ID)
Available from: 2018-09-25 Created: 2018-09-25 Last updated: 2019-10-31Bibliographically approved
Iftimi, A., Cronie, O. & Montes, F. (2019). Second-order analysis of marked inhomogeneous spatio-temporal point processes: applications to earthquake data. Scandinavian Journal of Statistics, 46(3), 661-685
Open this publication in new window or tab >>Second-order analysis of marked inhomogeneous spatio-temporal point processes: applications to earthquake data
2019 (English)In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 46, no 3, p. 661-685Article in journal (Refereed) Published
Abstract [en]

To analyse interactions in marked spatio-temporal point processes (MSTPPs), we introduce marked second-order reduced moment measures and K-functions for inhomogeneous second-order intensity reweighted stationary MSTPPs. These summary statistics, which allow us to quantify dependence between different mark-based classifications of the points, are depending on the specific mark space and mark reference measure chosen. Unbiased and consistent minus-sampling estimators are derived for all statistics considered and a test for random labelling is indicated. In addition, we treat Voronoi intensity estimators for MSTPPs. These new statistics are finally employed to analyse an Andaman sea earthquake data set.

Keywords
adaptive intensity estimation, earthquakes, marked inhomogeneous spatiotemporal point process, marked (second-order) intensity-reweighted stationarity, marked spatiotemporal K-function, marked spatiotemporal second-order reduced moment measure
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-153405 (URN)10.1111/sjos.12367 (DOI)000479058100001 ()
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-10-09Bibliographically approved
Toreti, A., Belward, A., Perez-Dominguez, I., Naumann, G., Luterbacher, J., Cronie, O., . . . Zampieri, M. (2019). The exceptional 2018 European water seesaw calls for action on adaptation. Earth's Future, 7(6), 652-663
Open this publication in new window or tab >>The exceptional 2018 European water seesaw calls for action on adaptation
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2019 (English)In: Earth's Future, ISSN 1384-5160, E-ISSN 2328-4277, Vol. 7, no 6, p. 652-663Article in journal (Refereed) Published
Abstract [en]

Temperature and precipitation are the most important factors responsible for agricultural productivity variations. In 2018 spring/summer growing season, Europe experienced concurrent anomalies of both. Drought conditions in central and northern Europe caused yield reductions up to 50% for the main crops, yet wet conditions in southern Europe saw yield gains up to 34%, both with respect to the previous 5‐years' mean. Based on the analysis of documentary and natural proxy based seasonal paleoclimate reconstructions for the past half millennium, we show that the 2018 combination of climatic anomalies in Europe was unique. The water seesaw, a marked dipole of negative water anomalies in central Europe and positive ones in southern Europe, distinguished 2018 from the five previous similar droughts since 1976. Model simulations reproduce the 2018 European water seesaw in only four years out of 875 years in historical runs and projections. Future projections under the RCP8.5 scenario show that 2018‐like temperature and rainfall conditions, favourable to crop growth, will occur less frequent in southern Europe. In contrast, in central Europe high‐end emission scenario climate projections show that droughts as intense as 2018 could become a common occurrence as early as 2043. Whilst integrated European and global agricultural markets limited agro‐economic shocks caused by 2018's extremes, there is an urgent need for adaptation strategies for European agriculture to consider futures without the benefits of any water seesaw.

Place, publisher, year, edition, pages
American Geophysical Union (AGU), 2019
Keywords
climate extremes, drought, water seesaw, Europe, agriculture, climate projections
National Category
Climate Research
Identifiers
urn:nbn:se:umu:diva-159155 (URN)10.1029/2019EF001170 (DOI)000474508700006 ()2-s2.0-85067691847 (Scopus ID)
Available from: 2019-05-20 Created: 2019-05-20 Last updated: 2019-10-09Bibliographically approved
Cronie, O. & Van Lieshout, M.-C. N. .. (2018). A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions. Biometrika, 105(2), 455-462
Open this publication in new window or tab >>A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions
2018 (English)In: Biometrika, ISSN 0006-3444, E-ISSN 1464-3510, Vol. 105, no 2, p. 455-462Article in journal (Refereed) Published
Abstract [en]

We propose a new bandwidth selection method for kernel estimators of spatial point process intensity functions. The method is based on an optimality criterion motivated by the Campbell formula applied to the reciprocal intensity function. The new method is fully nonparametric, does not require knowledge of higher-order moments, and is not restricted to a specific class of point process. Our approach is computationally straightforward and does not require numerical approximation of integrals.

Place, publisher, year, edition, pages
Oxford University Press, 2018
Keywords
Bandwidth selection, Campbell formula, Intensity function, Kernel estimation, Point process
National Category
Probability Theory and Statistics
Research subject
Statistics; Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-145022 (URN)10.1093/biomet/asy001 (DOI)000434111200014 ()
Available from: 2018-02-16 Created: 2018-02-16 Last updated: 2018-10-29Bibliographically approved
González, J. A., Rodríguez-Cortés, F. J., Cronie, O. & Mateu, J. (2016). Spatio-temporal point process statistics: a review. Spatial Statistics, 18(Part B), 505-544
Open this publication in new window or tab >>Spatio-temporal point process statistics: a review
2016 (English)In: Spatial Statistics, E-ISSN 2211-6753, Vol. 18, no Part B, p. 505-544Article, review/survey (Refereed) Published
Abstract [en]

Spatio-temporal point process data have been analysed quite a bit in specialised fields, with the aim of better understanding the inherent mechanisms that govern the temporal evolution of events placed in a planar region. In particular, in the last decade there has been an acceleration of methodological developments, accompanied by a broad collection of applications as spatiotemporally indexed data have become more widely available in many scientific fields. We present a self-contained review describing statistical models and methods that can be used to analyse patterns of points in space and time when the questions of scientific interest concern both their spatial and their temporal behaviour. We revisit moment characteristics that define summary statistics, as well as conditional intensities which uniquely characterise certain spatiotemporal point processes. We make use of these concepts to describe models and associated methods of inference for spatiotemporal point process data. Three new motivating real-data examples are described and analysed throughout the paper to illustrate the most relevant techniques, discussing the pros and cons of the different considered approaches.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Edge-correction, Empirical models, Intensity function, Mechanistic models, Second-order properties, Separability
National Category
Geology Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-132172 (URN)10.1016/j.spasta.2016.10.002 (DOI)000393232900012 ()
Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2018-06-09Bibliographically approved
Cronie, O. & van Lieshout, M. N. (2016). Summary statistics for inhomogeneous marked point processes. Annals of the Institute of Statistical Mathematics, 68(4), 905-928
Open this publication in new window or tab >>Summary statistics for inhomogeneous marked point processes
2016 (English)In: Annals of the Institute of Statistical Mathematics, ISSN 0020-3157, E-ISSN 1572-9052, Vol. 68, no 4, p. 905-928Article in journal (Refereed) Published
Abstract [en]

We propose new summary statistics for intensity-reweighted moment stationary marked point processes with particular emphasis on discrete marks. The new statistics are based on the -point correlation functions and reduce to cross - and -functions when stationarity holds. We explore the relationships between the various functions and discuss their explicit forms under specific model assumptions. We derive ratio-unbiased minus sampling estimators for our statistics and illustrate their use on a data set of wildfires.

Keywords
Generating functional, Intensity-reweighted moment stationarity, J-function, Marked point process, ltivariate point process, Nearest neighbour distance distribution function, n-point correlation nction, Reduced Palm measure
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-124314 (URN)10.1007/s10463-015-0515-z (DOI)000379513000009 ()
External cooperation:
Available from: 2016-09-06 Created: 2016-08-04 Last updated: 2018-06-07Bibliographically approved
Cronie, O. & Yu, J. (2016). The discretely observed immigration-death process: Likelihood inference and spatiotemporal applications. Communications in Statistics - Theory and Methods, 45(18), 5279-5298
Open this publication in new window or tab >>The discretely observed immigration-death process: Likelihood inference and spatiotemporal applications
2016 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 45, no 18, p. 5279-5298Article in journal (Refereed) Published
Abstract [en]

We consider a stochastic process, the homogeneous spatial immigration-death (HSID) process, which is a spatial birth-death process with as building blocks (i) an immigration-death (ID) process (a continuous-time Markov chain) and (ii) a probability distribution assigning iid spatial locations to all events. For the ID process, we derive the likelihood function, reduce the likelihood estimation problem to one dimension, and prove consistency and asymptotic normality for the maximum likelihood estimators (MLEs) under a discrete sampling scheme. We additionally prove consistency for the MLEs of HSID processes. In connection to the growth-interaction process, which has a HSID process as basis, we also fit HSID processes to Scots pine data.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2016
Keywords
Asymptotic normality, Consistency, Homogenous spatial immigration-death process, Maximum likelihood, Spatial birth–death process, Spatiotemporal growth-interaction process
National Category
Probability Theory and Statistics Forest Science
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-124363 (URN)10.1080/03610926.2014.942433 (DOI)000380898500003 ()
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Available from: 2016-08-05 Created: 2016-08-05 Last updated: 2018-06-07Bibliographically approved
Iftimi, A., Cronie, O. & Montes, F. (2016). The second-order statistical analysis of marked inhomogeneous spatio-temporal point processes. In: A. Iftimi, J. Mateu, F. Montes (Ed.), METMA VIII: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling. Paper presented at 8th International Workshop on Spatio-Temporal Modelling (METMA VIII), Valencia (Spain), 1–3 June 2016 (pp. 89-93).
Open this publication in new window or tab >>The second-order statistical analysis of marked inhomogeneous spatio-temporal point processes
2016 (English)In: METMA VIII: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling / [ed] A. Iftimi, J. Mateu, F. Montes, 2016, p. 89-93Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

An earthquake is characterised by the shaking of the surface of the Earth, and can range from being imperceptible to causing huge damage and killing thousands of people. Magnitude is a measure of the size of an earthquake source, and does not depend on the spatio-temporal location of the event. The data in this study includes all earthquakes with magnitude larger than or equal to 5, with a total of 1248 earthquakes registered from 2004 to 2008, in the area of Sumatra (Indonesia). We analyse the interaction between different types of earthquakes, classified according to their magnitude, at different space-time scales. We want to identify spatio-temporal interaction between high magnitude earthquakes (magnitude larger than 5:5) and smaller magnitude earthquakes ( 5:5). The analysis shows a strong relation between big earthquakes and aftershocks. We observe that aftershocks could span their power as far as 3,000 km and 400 days. Random labelling testing shows, as expected, that we do not have random labelling.

Keywords
Second-order analysis, Marked inhomogeneous point pattern, Earthquake data, Marked inhomogeneous spatio-temporal K-function
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics; Statistics
Identifiers
urn:nbn:se:umu:diva-145041 (URN)978-84-608-8468-2 (ISBN)
Conference
8th International Workshop on Spatio-Temporal Modelling (METMA VIII), Valencia (Spain), 1–3 June 2016
Available from: 2018-02-18 Created: 2018-02-18 Last updated: 2018-06-09Bibliographically approved
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