Valid Resampling of Higher-Order Statistics Using the Linear Process Bootstrap and Autoregressive Sieve Bootstrap

DOI10.1080/03610926.2012.698781zbMath1347.62064OpenAlexW2015320971MaRDI QIDQ4929188

Dimitris N. Politis, Carsten Jentsch

Publication date: 13 June 2013

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2012.698781

zbMATH Keywords

block bootstrapsample autocovariancesgeneralized meansAR sieve bootstraplinear process bootstrapresampling of blocks

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric statistical resampling methods (62G09)

Related Items (3)

Bootstrapping INAR models ⋮ Guaranteed conditional ARL performance in the presence of autocorrelation ⋮ Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension

Cites Work

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• The multiple hybrid bootstrap -- resampling multivariate linear processes
• On the range of validity of the autoregressive sieve bootstrap
• Bootstrap methods for dependent data: a review
• Simple consistent estimation of the coefficients of a linear filter
• Time series: theory and methods.
• A general resampling scheme for triangular arrays of \(\alpha\)-mixing random variables with application to the problem of spectral density estimation
• Sieve bootstrap for time series
• The jackknife and the bootstrap for general stationary observations
• Banded and tapered estimates for autocovariance matrices and the linear process bootstrap

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