Bootstrapping the empirical distribution of a linear process

DOI10.1016/J.SPL.2014.06.019zbMath1463.62127OpenAlexW2060778716MaRDI QIDQ395990

Farid El Ktaibi, B. Gail Ivanoff, Neville C. Weber

Publication date: 8 August 2014

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2014.06.019

zbMATH Keywords

empirical processgoodness of fitcausal linear processmoving block bootstrap

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Order statistics; empirical distribution functions (62G30) Stationary stochastic processes (60G10) Nonparametric statistical resampling methods (62G09)

Related Items (4)

Incorporating a change-point estimator when bootstrapping the empirical distribution of a stationary process ⋮ Weak convergence for stationary bootstrap empirical processes of associated sequences ⋮ Weak convergence of stationary empirical processes ⋮ Bootstrapping the empirical distribution of a stationary process with change-point

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Another look at the disjoint blocks bootstrap
• Some mixing properties of time series models
• Weak convergence of dependent empirical measures with application to subsampling in function spaces
• On the blockwise bootstrap for empirical processes for stationary sequences
• Limit theorems for functionals of moving averages
• The bootstrap for empirical processes based on stationary observations
• Resampling methods for dependent data
• The jackknife and the bootstrap for general stationary observations
• Bootstraps for time series
• Weak convergence and empirical processes. With applications to statistics
• Bootstrap for the sample mean and forU-statistics of mixing and near-epoch dependent processes
• Necessary and sufficient conditions for the moving blocks bootstrap central limit theorem of the mean
• On the Strong Mixing Property for Linear Sequences
• Functional central limit theorem for the empirical process of short memory linear processes

This page was built for publication: Bootstrapping the empirical distribution of a linear process