Pub Date : 2022-02-14DOI: 10.1007/s10687-023-00463-z
Zhongwei Zhang, E. Krainski, P. Zhong, H. Rue, Raphael Huser
{"title":"Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach","authors":"Zhongwei Zhang, E. Krainski, P. Zhong, H. Rue, Raphael Huser","doi":"10.1007/s10687-023-00463-z","DOIUrl":"https://doi.org/10.1007/s10687-023-00463-z","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42513152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-16DOI: 10.1007/s10687-022-00459-1
T. Ivek, Domagoj Vlah
{"title":"Reconstruction of incomplete wildfire data using deep generative models","authors":"T. Ivek, Domagoj Vlah","doi":"10.1007/s10687-022-00459-1","DOIUrl":"https://doi.org/10.1007/s10687-022-00459-1","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49534225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-10-21DOI: 10.1007/s10687-022-00446-6
Miguel de Carvalho, Alina Kumukova, Gonçalo Dos Reis
This paper devises a regression-type model for the situation where both the response and covariates are extreme. The proposed approach is designed for the setting where the response and covariates are modeled as multivariate extreme values, and thus contrarily to standard regression methods it takes into account the key fact that the limiting distribution of suitably standardized componentwise maxima is an extreme value copula. An important target in the proposed framework is the regression manifold, which consists of a family of regression lines obeying the latter asymptotic result. To learn about the proposed model from data, we employ a Bernstein polynomial prior on the space of angular densities which leads to an induced prior on the space of regression manifolds. Numerical studies suggest a good performance of the proposed methods, and a finance real-data illustration reveals interesting aspects on the conditional risk of extreme losses in two leading international stock markets.
Supplementary information: The online version contains supplementary material available at 10.1007/s10687-022-00446-6.
{"title":"Regression-type analysis for multivariate extreme values.","authors":"Miguel de Carvalho, Alina Kumukova, Gonçalo Dos Reis","doi":"10.1007/s10687-022-00446-6","DOIUrl":"https://doi.org/10.1007/s10687-022-00446-6","url":null,"abstract":"<p><p>This paper devises a regression-type model for the situation where both the response and covariates are extreme. The proposed approach is designed for the setting where the response and covariates are modeled as multivariate extreme values, and thus contrarily to standard regression methods it takes into account the key fact that the limiting distribution of suitably standardized componentwise maxima is an extreme value copula. An important target in the proposed framework is the regression manifold, which consists of a family of regression lines obeying the latter asymptotic result. To learn about the proposed model from data, we employ a Bernstein polynomial prior on the space of angular densities which leads to an induced prior on the space of regression manifolds. Numerical studies suggest a good performance of the proposed methods, and a finance real-data illustration reveals interesting aspects on the conditional risk of extreme losses in two leading international stock markets.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s10687-022-00446-6.</p>","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9589765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40458093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-02-16DOI: 10.1007/s10687-022-00436-8
Martin Bladt, Jorge Yslas
A phase-type distribution is the distribution of the time until absorption in a finite state-space time-homogeneous Markov jump process, with one absorbing state and the rest being transient. These distributions are mathematically tractable and conceptually attractive to model physical phenomena due to their interpretation in terms of a hidden Markov structure. Three recent extensions of regular phase-type distributions give rise to models which allow for heavy tails: discrete- or continuous-scaling; fractional-time semi-Markov extensions; and inhomogeneous time-change of the underlying Markov process. In this paper, we present a unifying theory for heavy-tailed phase-type distributions for which all three approaches are particular cases. Our main objective is to provide useful models for heavy-tailed phase-type distributions, but any other tail behavior is also captured by our specification. We provide relevant new examples and also show how existing approaches are naturally embedded. Subsequently, two multivariate extensions are presented, inspired by the univariate construction which can be considered as a matrix version of a frailty model. We provide fully explicit EM-algorithms for all models and illustrate them using synthetic and real-life data.
{"title":"Heavy-tailed phase-type distributions: a unified approach.","authors":"Martin Bladt, Jorge Yslas","doi":"10.1007/s10687-022-00436-8","DOIUrl":"https://doi.org/10.1007/s10687-022-00436-8","url":null,"abstract":"<p><p>A phase-type distribution is the distribution of the time until absorption in a finite state-space time-homogeneous Markov jump process, with one absorbing state and the rest being transient. These distributions are mathematically tractable and conceptually attractive to model physical phenomena due to their interpretation in terms of a hidden Markov structure. Three recent extensions of regular phase-type distributions give rise to models which allow for heavy tails: discrete- or continuous-scaling; fractional-time semi-Markov extensions; and inhomogeneous time-change of the underlying Markov process. In this paper, we present a unifying theory for heavy-tailed phase-type distributions for which all three approaches are particular cases. Our main objective is to provide useful models for heavy-tailed phase-type distributions, but any other tail behavior is also captured by our specification. We provide relevant new examples and also show how existing approaches are naturally embedded. Subsequently, two multivariate extensions are presented, inspired by the univariate construction which can be considered as a matrix version of a frailty model. We provide fully explicit EM-algorithms for all models and illustrate them using synthetic and real-life data.</p>","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40568768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-31DOI: 10.1007/s10687-023-00472-y
G. Last
{"title":"Tail processes and tail measures: An approach via Palm calculus","authors":"G. Last","doi":"10.1007/s10687-023-00472-y","DOIUrl":"https://doi.org/10.1007/s10687-023-00472-y","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44149630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-31DOI: 10.1007/s10687-023-00469-7
E. D’Arcy, C. J. R. Murphy-Barltrop, R. Shooter, E. Simpson
{"title":"A marginal modelling approach for predicting wildfire extremes across the contiguous United States","authors":"E. D’Arcy, C. J. R. Murphy-Barltrop, R. Shooter, E. Simpson","doi":"10.1007/s10687-023-00469-7","DOIUrl":"https://doi.org/10.1007/s10687-023-00469-7","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44830084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-30DOI: 10.1007/s10687-022-00460-8
Daniela Cisneros, Yan Gong, Rishikesh Yadav, A. Hazra, Raphael Huser
{"title":"A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes","authors":"Daniela Cisneros, Yan Gong, Rishikesh Yadav, A. Hazra, Raphael Huser","doi":"10.1007/s10687-022-00460-8","DOIUrl":"https://doi.org/10.1007/s10687-022-00460-8","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48121423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-23DOI: 10.1007/s10687-021-00429-z
Hui Xu, R. Davis, G. Samorodnitsky
{"title":"Handling missing extremes in tail estimation","authors":"Hui Xu, R. Davis, G. Samorodnitsky","doi":"10.1007/s10687-021-00429-z","DOIUrl":"https://doi.org/10.1007/s10687-021-00429-z","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47877604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-20DOI: 10.1007/s10687-022-00440-y
Liuju Chen, Deyuan Li, Chen Zhou
{"title":"Adapting the Hill estimator to distributed inference: dealing with the bias","authors":"Liuju Chen, Deyuan Li, Chen Zhou","doi":"10.1007/s10687-022-00440-y","DOIUrl":"https://doi.org/10.1007/s10687-022-00440-y","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45083309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-09DOI: 10.1007/s10687-023-00467-9
Stefka Asenova, J. Segers
{"title":"Extremes of Markov random fields on block graphs: Max-stable limits and structured Hüsler–Reiss distributions","authors":"Stefka Asenova, J. Segers","doi":"10.1007/s10687-023-00467-9","DOIUrl":"https://doi.org/10.1007/s10687-023-00467-9","url":null,"abstract":"","PeriodicalId":49274,"journal":{"name":"Extremes","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42154564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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