Bootstrapping the empirical distribution function of a spatial process

DOI10.1007/s11203-005-2349-4zbMath1110.62127OpenAlexW2008916953MaRDI QIDQ882912

Jun Zhu, Soumendra Nath Lahiri

Publication date: 24 May 2007

Published in: Statistical Inference for Stochastic Processes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11203-005-2349-4

zbMATH Keywords

functional central limit theoremresamplinginfill asymptoticsincreasing domain asymptoticsalpha-mixing random field

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Central limit and other weak theorems (60F05) Nonparametric statistical resampling methods (62G09) Functional limit theorems; invariance principles (60F17)

Related Items (3)

Blockwise bootstrap of the estimated empirical process based on \(\psi \)-weakly dependent observations ⋮ Change-point detection and bootstrap for Hilbert space valued random fields ⋮ Properties of spatial cross-periodograms using fixed-domain asymptotics

Uses Software

• bootlib

Cites Work

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• Approximation theorems for strongly mixing random variables
• Resampling a coverage pattern
• On the central limit theorem for stationary mixing random fields
• Bootstrap methods: another look at the jackknife
• Asymptotic distribution of the empirical spatial cumulative distribution function predictor and prediction bands based on a subsampling method
• Central limit theorems for empirical and \(U\)-processes of stationary mixing sequences
• Nonparametric resampling for homogeneous strong mixing random fields
• Properties of nonparametric estimators of autocovariance for stationary random fields
• Validity of blockwise bootstrap for empirical processes with stationary observations
• Blockwise bootstrapped empirical process for stationary sequences
• Weak convergence of multidimensional empirical processes for stationary \(\varphi\)-mixing processes
• On optimal spatial subsample size for variance estimation
• Asymptotic distributions of M-estimators in a spatial regression model under some fixed and stochastic spatial sampling designs
• The jackknife and the bootstrap for general stationary observations
• A note on empirical processes of strong-mixing sequences
• Large sample confidence regions based on subsamples under minimal assumptions
• SOME THEORY ON M-SMOOTHING OF TIME SERIES
• Weak convergence of multidimensional empirical processes for strong mixing sequences of stochastic vectors
• Moment bounds for stationary mixing sequences
• Subsampling Continuous Parameter Random Fields and a Bernstein Inequality
• Convergence Criteria for Multiparameter Stochastic Processes and Some Applications

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