Diagnostic check for heavy tail in linear time series

DOI10.1016/J.STAMET.2014.11.001zbMath1486.62247OpenAlexW2024508099MaRDI QIDQ1731253

Tony Siu Tung Wong

Publication date: 13 March 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2014.11.001

zbMATH Keywords

residualextreme value indexchange pointpeaks-over-thresholdautoregressive moving-average model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistics of extreme values; tail inference (62G32)

Uses Software

• ismev

Cites Work

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• Extreme quantile estimation for dependent data, with applications to finance
• Extremes and related properties of random sequences and processes
• Point processes and multivariate extreme values
• Statistical inference using extreme order statistics
• Estimating the dimension of a model
• Inference for the tail parameters of a linear process with heavy tail innovations
• Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. With comments and a rejoinder by the author
• The supremum of a negative drift random walk with dependent heavy-tailed steps.
• Modelling the persistence of conditional variances
• Tests for a change-point
• The likelihood ratio test for a change-point in simple linear regression
• A new look at the Bayes procedure
• Autoregressive Conditional Density Estimation
• Statistics of Extremes
• Modeling Waves of Extreme Temperature: The Changing Tails of Four Cities
• An introduction to statistical modeling of extreme values

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