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Cronie, O., Moradi, M. & Mateu, J. (2020). Inhomogeneous higher-order summary statistics for point processes on linear networks. Statistics and computing
Open this publication in new window or tab >>Inhomogeneous higher-order summary statistics for point processes on linear networks
2020 (English)In: Statistics and computing, ISSN 0960-3174, E-ISSN 1573-1375Article in journal (Refereed) Epub ahead of print
Abstract [en]

As a workaround for the lack of transitive transformations on linear network structures, which are required to consider different notions of distributional invariance, including stationarity, we introduce the notions of pseudostationarity and intensity reweighted moment pseudostationarity for point processes on linear networks. Moreover, using arbitrary so-called regular linear network distances, e.g. the Euclidean and the shortest-path distance, we further propose geometrically corrected versions of different higher-order summary statistics, including the inhomogeneous empty space function, the inhomogeneous nearest neighbour distance distribution function and the inhomogeneous J-function. Such summary statistics detect interactions of order higher than two. We also discuss their nonparametric estimators and through a simulation study, considering models with different types of spatial interaction and different networks, we study the performance of our proposed summary statistics by means of envelopes. Our summary statistic estimators manage to capture clustering, regularity as well as Poisson process independence. Finally, we make use of our new summary statistics to analyse two different datasets: motor vehicle traffic accidents and spiderwebs.

Place, publisher, year, edition, pages
Springer, 2020
Keywords
Inhomogeneous linear empty space function, Inhomogeneous linear J-function, Inhomogeneous linear nearest neighbour distance distribution function, Linear network, Pseudostationarity, Regular distance metric, Traffic accident data
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-170821 (URN)10.1007/s11222-020-09942-w (DOI)000528407500001 ()
Available from: 2020-05-26 Created: 2020-05-26 Last updated: 2020-05-26
Mateu, J., Moradi, M. M. & Cronie, O. (2020). Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation. Spatial Statistics
Open this publication in new window or tab >>Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation
2020 (English)In: Spatial Statistics, E-ISSN 2211-6753Article in journal (Refereed) Epub ahead of print
Abstract [en]

Aside from reviewing different intensity estimation schemes for point processes on linear networks, this paper introduces two Voronoi-based intensity estimation approaches for spatio-temporal linear network point processes. The first is a separable estimator, which is obtained as a scaled product of a resample-smoothed Voronoi intensity estimator on the linear network in question and another one on the time domain. The second one, which we refer to as a pseudo-separable resample-smoothed Voronoi intensity estimator, uses a slightly different thinning strategy. Through a simulation study we show that the latter performs slightly better than the former. We finally apply the latter estimator to a spatio-temporal traffic accident point pattern.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Intensity estimation, Linear network, Pseudo-separability, Resample-smoothing, Spatio-temporal point process, Voronoi estimator
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-167195 (URN)10.1016/j.spasta.2019.100400 (DOI)
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-13
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
Cronie, O., Ghorbani, M., Mateu, J. & Yu, J. (2019). Functional marked point processes: A natural structure to unify spatio-temporal frameworks and to analyse dependent functional data.
Open this publication in new window or tab >>Functional marked point processes: A natural structure to unify spatio-temporal frameworks and to analyse dependent functional data
2019 (English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper treats functional marked point processes (FMPPs), which are defined as marked point processes where the marks are random elements in some (Polish) function space. Such marks may represent e.g. spatial paths or functions of time. To be able to consider e.g. multivariate FMPPs, we also attach an additional, Euclidean, mark to each point. We indicate how FMPPs quite naturally connect the point process framework with both the functional data analysis framework and the geostatistical framework. We further show that various existing models fit well into the FMPP framework. In addition, we introduce a new family of summary statistics, weighted marked reduced moment measures, together with their non-parametric estimators, in order to study features of the functional marks. We further show how they generalise other summary statistics and we finally apply these tools to analyse population structures, such as demographic evolution and sex ratio over time, in Spanish provinces.

Publisher
p. 44
Keywords
Correlation functional, Functional data analysis, Intensity functional, Marked point process, Nonparametric estimation, Palm distribution, Population growth, Spatio-temporal geostatistical marking, Weighted marked reduced moment measure
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-165644 (URN)
Projects
Large scale analysis of tree growth in space and time under changing climate conditions
Funder
The Kempe Foundations, SMK-1750
Available from: 2019-12-02 Created: 2019-12-02 Last updated: 2019-12-03
Cronie, O., Ghorbani, M., Yu, J. & Mateu, J. (2019). Functional marked point processes: Unifying spatio-temporal frameworks and analysing spatially dependent functional data. In: Statistical Analysis for Space-Time Data: Pragramme and Abstract Book. Paper presented at ECAS 2019 on Statistical Analysis for Space-Time Data. Lisboa, Portugal, July 15-17, 2019 (pp. 7-7). Eurpean Courses in Advanced Statistics (ECAS)
Open this publication in new window or tab >>Functional marked point processes: Unifying spatio-temporal frameworks and analysing spatially dependent functional data
2019 (English)In: Statistical Analysis for Space-Time Data: Pragramme and Abstract Book, Eurpean Courses in Advanced Statistics (ECAS) , 2019, p. 7-7Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

This paper treats functional marked point processes (FMPPs), which are defined as marked point processes where the marks are random elements in some (Polish) function space. Such marks may represent e.g. spatial paths or functions of time. To be able to consider e.g. multivariate FMPPs, we also attach an additionally, Euclidean, mark to each point. We indicate how the FMPP framework quite naturally connects the point process framework with both the functional data analysis framework and the geostatistical framework; in particular we define spatio-temporal geostatistical marking for point processes. We further show that various existing stochastic models fit well into the FMPP framework, in particular marked point processes with real valued marks. To be able to carry out non-parametric statistical analyses for functional marked point patterns, we study characteristics such as product densities and Palm distributions, which are the building blocks for summary statistics such as marked inhomogeneous J-functions and our so-called K-functionals. We finally apply these statistical tools to analyse a few different functional marked point patterns.

Place, publisher, year, edition, pages
Eurpean Courses in Advanced Statistics (ECAS), 2019
Keywords
C`adl`ag stochastic process, Correlation functional, Functional marked point process, Intensity functional, Marked inhomogeneous K-functional, Spatiotemporal geostatistical marking
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:umu:diva-161564 (URN)
Conference
ECAS 2019 on Statistical Analysis for Space-Time Data. Lisboa, Portugal, July 15-17, 2019
Projects
Large scale analysis of tree growth in space and time under changing climate conditions
Funder
The Kempe Foundations, SMK-1750
Available from: 2019-07-11 Created: 2019-07-11 Last updated: 2020-02-07Bibliographically 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
Show others...
2019 (English)In: Statistics and computing, ISSN 0960-3174, E-ISSN 1573-1375, Vol. 29, no 5, p. 995-1010Article in journal (Refereed) Published
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: 2019-12-19Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6721-8608

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