Extremes最新文献_第5页

Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach 基于INLA-SPDE方法的大规模野火数量和燃烧面积的联合建模与预测

IF 1.3 3区数学

Extremes Pub Date : 2022-02-14 DOI: 10.1007/s10687-023-00463-z

Zhongwei Zhang, E. Krainski, P. Zhong, H. Rue, Raphael Huser

引用次数: 4

Reconstruction of incomplete wildfire data using deep generative models 使用深度生成模型重建不完整的野火数据

IF 1.3 3区数学

Extremes Pub Date : 2022-01-16 DOI: 10.1007/s10687-022-00459-1

T. Ivek, Domagoj Vlah

引用次数: 4

Regression-type analysis for multivariate extreme values. 多元极值的回归型分析。

IF 1.3 3区数学

Extremes Pub Date : 2022-01-01 Epub Date: 2022-10-21 DOI: 10.1007/s10687-022-00446-6

Miguel de Carvalho, Alina Kumukova, Gonçalo Dos Reis

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引用次数: 1

Heavy-tailed phase-type distributions: a unified approach. 重尾相型分布:统一的方法。

IF 1.3 3区数学

Extremes Pub Date : 2022-01-01 Epub Date: 2022-02-16 DOI: 10.1007/s10687-022-00436-8

Martin Bladt, Jorge Yslas

{"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":"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.","PeriodicalId":49274,"journal":{"name":"Extremes","volume":"25 3","pages":"529-565"},"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}

引用次数: 1

Tail processes and tail measures: An approach via Palm calculus 尾部过程和尾部度量：一种基于Palm演算的方法

IF 1.3 3区数学

Extremes Pub Date : 2021-12-31 DOI: 10.1007/s10687-023-00472-y

G. Last

引用次数: 4

A marginal modelling approach for predicting wildfire extremes across the contiguous United States 预测美国各地极端野火的边际建模方法

IF 1.3 3区数学

Extremes Pub Date : 2021-12-31 DOI: 10.1007/s10687-023-00469-7

E. D’Arcy, C. J. R. Murphy-Barltrop, R. Shooter, E. Simpson

引用次数: 1

A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes 用于极端野火频率和规模空间预测的统计和机器学习相结合的方法