Bootstrap methods for dependent data: a review

DOI10.1016/j.jkss.2011.08.009zbMath1296.62172OpenAlexW2032270307MaRDI QIDQ743759

Efstathios Paparoditis, Jens-Peter Kreiss

Publication date: 30 September 2014

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jkss.2011.08.009

zbMATH Keywords

time seriesstochastic processesbootstrap methods

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15) Nonparametric statistical resampling methods (62G09) Research exposition (monographs, survey articles) pertaining to statistics (62-02)

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Uses Software

• bootstrap
• bootlib

Cites Work

• Asymptotic and Bootstrap Inference for AR(∞) Processes with Conditional Heteroskedasticity
• Inference in Arch and Garch Models with Heavy-Tailed Errors
• Residual-Based Block Bootstrap for Unit Root Testing
• Bootstrap Methods for Markov Processes
• Higher-Order Improvements of a Computationally Attractive k-Step Bootstrap for Extremum Estimators
• the Block-Block Bootstrap: Improved Asymptotic Refinements
• Bootstrapping Unit Root Tests for Autoregressive Time Series
• The bootstrap and Edgeworth expansion
• The impact of bootstrap methods on time series analysis
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• Sieve bootstrap for smoothing in nonstationary time series
• Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White
• Unit root testing via the stationary bootstrap
• Bootstrap specification tests for linear covariance stationary processes
• Properties of a block bootstrap under long-range dependence
• Bootstrapping realized multivariate volatility measures
• The multiple hybrid bootstrap -- resampling multivariate linear processes
• TFT-bootstrap: resampling time series in the frequency domain to obtain replicates in the time domain
• On the range of validity of the autoregressive sieve bootstrap
• Bootstrap tests for simple structures in nonparametric time series regression
• A nonparametric plug-in rule for selecting optimal block lengths for block bootstrap methods
• Bootstrap in Markov-sequences based on estimates of transition density
• On bootstrapping two-stage least-squares estimates in stationary linear models
• Asymptotic normality, strong mixing and spectral density estimates
• Bootstrap prediction for returns and volatilities in GARCH models
• A bootstrap test for time series linearity
• A note on the stationary bootstrap's variance
• Goodness-of-fit tests for Markovian time series models: central limit theory and bootstrap approximations
• Asymptotic normality of spectral estimates
• Resampling a coverage pattern
• The use of subseries values for estimating the variance of a general statistic from a stationary sequence
• Edgeworth correction by bootstrap in autoregressions
• Bootstrapping general first order autoregression
• Time series: theory and methods.
• On bootstrapping kernel spectral estimates
• Bootstrapping Markov chains: Countable case
• A general resampling scheme for triangular arrays of \(\alpha\)-mixing random variables with application to the problem of spectral density estimation
• Bootstrap methods: another look at the jackknife
• Matched-block bootstrap for dependent data
• A new mixing notion and functional central limit theorems for a sieve bootstrap in time series
• Subsampling
• On the moving block bootstrap under long range dependence
• Blockwise bootstrapped empirical process for stationary sequences
• Second-order correctness of the blockwise bootstrap for stationary observations
• A frequency domain bootstrap for ratio statistics in time series analysis
• Sieve bootstrap for time series
• Resampling methods for dependent data
• An alternative bootstrap to moving blocks for time series regression models
• A sieve bootstrap test for stationarity.
• Autoregressive-aided periodogram bootstrap for time series
• Local block bootstrap
• The local bootstrap for kernel estimators under general dependence conditions
• Block length selection in the bootstrap for time series
• Bootstrap of kernel smoothing in nonlinear time series
• Regression-type inference in nonparametric autoregression
• Theoretical comparisons of block bootstrap methods
• Jackknife, bootstrap and other resampling methods in regression analysis
• The jackknife and the bootstrap for general stationary observations
• The local bootstrap for Markov processes
• Bootstraps for time series
• The jackknife and bootstrap
• Bootstrapping autoregressive and moving average parameter estimates of infinite order vector autoregressive processes
• On asymptotic properties of bootstrap for AR(1) processes
• Consistency of the stationary bootstrap under weak moment conditions
• Bootstrapping unstable first-order autoregressive processes
• Asymptotic spectral theory for nonlinear time series
• Blockwise bootstrap testing for stationarity
• Bootstrap in moving average models
• Bootstrap Tests for an Autoregressive Unit Root in the Presence of Weakly Dependent Errors
• Tapered block bootstrap
• Bootstrapping Frequency Domain Tests in Multivariate Time Series with an Application to Comparing Spectral Densities
• Banded and tapered estimates for autocovariance matrices and the linear process bootstrap
• Bootstrap Inference for a First-Order Autoregression with Positive Innovations
• Bootstrap autoregressive order selection
• Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes
• Bootstrapping the Local Periodogram of Locally Stationary Processes
• Resampling and Subsampling for Financial Time Series
• ORDER IDENTIFICATION STATISTICS IN STATIONARY AUTOREGRESSIVE MOVING-AVERAGE MODELS:VECTOR AUTOCORRELATIONS AND THE BOOTSTRAP
• Asymptotic Statistics
• ON STUDENTIZING AND BLOCKING METHODS FOR IMPLEMENTING THE BOOTSTRAP WITH DEPENDENT DATA
• The Stationary Bootstrap
• A Frequency Domain Bootstrap-Based Method for Checking the Fit of a Transfer Function Model
• Automatic Block-Length Selection for the Dependent Bootstrap
• Recent developments in bootstrapping time series
• Bootstrap Confidence Regions Computed from Autoregressions of Arbitrary Order
• The tapered block bootstrap for general statistics from stationary sequences
• Properties of the nonparametric autoregressive bootstrap
• BOOTSTRAPPING STATIONARY AUTOREGRESSIVE MOVING‐AVERAGE MODELS
• A MARKOVIAN LOCAL RESAMPLING SCHEME FOR NONPARAMETRIC ESTIMATORS IN TIME SERIES ANALYSIS
• AN INVARIANCE PRINCIPLE FOR SIEVE BOOTSTRAP IN TIME SERIES
• Bootstrap Techniques for Signal Processing
• Bootstrap Methods for Time Series
• On blocking rules for the bootstrap with dependent data
• Bootstrapping time series models
• The Local Bootstrap for Periodogram Statistics
• The Dependent Wild Bootstrap
• Bootstrapping Locally Stationary Processes

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