Convergence rates of empirical block length selectors for block bootstrap

DOI10.3150/13-BEJ511zbMath1400.62088arXiv1403.3275MaRDI QIDQ2448717

Daniel J. Nordman, Soumendra Nath Lahiri

Publication date: 5 May 2014

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1403.3275

zbMATH Keywords

variance estimationmoving block bootstrap

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09)

Related Items (3)

Discussion of: ``Models as approximations ⋮ Convergence rates of empirical block length selectors for block bootstrap ⋮ On optimal block resampling for Gaussian-subordinated long-range dependent processes

Cites Work

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• Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White
• A nonparametric plug-in rule for selecting optimal block lengths for block bootstrap methods
• Bandwidth choice for nonparametric regression
• A note on the stationary bootstrap's variance
• On the amount of noise inherent in bandwidth selection for a kernel density estimator
• Resampling methods for dependent data
• Block length selection in the bootstrap for time series
• Theoretical comparisons of block bootstrap methods
• The jackknife and the bootstrap for general stationary observations
• Large sample confidence regions based on subsamples under minimal assumptions
• Convergence rates of empirical block length selectors for block bootstrap
• Tapered block bootstrap
• BIAS-CORRECTED NONPARAMETRIC SPECTRAL ESTIMATION
• Automatic Block-Length Selection for the Dependent Bootstrap
• Adaptive bandwidth choice
• ON THE JACKKNIFE-AFTER-BOOTSTRAP METHOD FOR DEPENDENT DATA AND ITS CONSISTENCY PROPERTIES
• On blocking rules for the bootstrap with dependent data
• LOCALLY ADAPTIVE LAG-WINDOW SPECTRAL ESTIMATION
• The bootstrap and Edgeworth expansion

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