Inference for heavy tailed stationary time series based on sliding blocks

DOI10.1214/18-EJS1415zbMath1473.62303arXiv1706.01968OpenAlexW2622347804MaRDI QIDQ1746555

Axel Bücher, Johan Segers

Publication date: 25 April 2018

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1706.01968

zbMATH Keywords

maximum likelihood estimatorFréchet distributionPickands dependence functionMarshall-Olkin distributionreturn levelblock maximaApéry's constant

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistics of extreme values; tail inference (62G32) Economic time series analysis (91B84)

Related Items (8)

Estimation of cluster functionals for regularly varying time series: runs estimators ⋮ Multiple block sizes and overlapping blocks for multivariate time series extremes ⋮ Tail inference using extreme U-statistics ⋮ On the disjoint and sliding block maxima method for piecewise stationary time series ⋮ Asymptotics for sliding blocks estimators of rare events ⋮ Estimation of cluster functionals for regularly varying time series: sliding blocks estimators ⋮ A horse race between the block maxima method and the peak-over-threshold approach ⋮ Modelling sub-daily precipitation extremes with the blended generalised extreme value distribution

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