Inference for the tail parameters of a linear process with heavy tail innovations

DOI10.1023/A:1003499300817zbMath0903.62069OpenAlexW2059210106MaRDI QIDQ1265223

William P. McCormick, Somnath Datta

Publication date: 6 January 1999

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1003499300817

zbMATH Keywords

consistencytail probabilityasymptotic normalitytime serieslinear processheavy tailed distributiontail parameters

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Non-Markovian processes: estimation (62M09)

Related Items (11)

Hill's estimator for the tail index of an ARMA model ⋮ On consistency of the likelihood moment estimators for a linear process with regularly varying innovations ⋮ Test for tail index change in stationary time series with Pareto-type marginal distribution ⋮ Asymptotic properties of the tail distribution and Hill's estimator for shot noise sequence ⋮ An adaptive optimal estimate of the tail index for MA(1) time series ⋮ Diagnostic check for heavy tail in linear time series ⋮ Asymptotic normality of the likelihood moment estimators for a stationary linear process with heavy-tailed innovations ⋮ Sequential monitoring of the tail behavior of dependent data ⋮ Some aspects of extreme value statistics under serial dependence ⋮ Weak convergence of the tail empirical process for dependent sequences ⋮ Estimation of the index parameter for autoregressive data using the estimated innovations

This page was built for publication: Inference for the tail parameters of a linear process with heavy tail innovations