Consistency of a hybrid block bootstrap for distribution and variance estimation for sample quantiles of weakly dependent sequences
DOI10.1111/anzs.12206OpenAlexW2775208381MaRDI QIDQ4639817
G. Alastair Young, Stephen M. S. Lee, Todd A. Kuffner
Publication date: 11 May 2018
Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10044/1/50036
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Inequalities; stochastic orderings (60E15) Bootstrap, jackknife and other resampling methods (62F40) Nonparametric statistical resampling methods (62G09)
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- A Berry-Esseen theorem for sample quantiles under weak dependence
- Resampling a coverage pattern
- The use of subseries values for estimating the variance of a general statistic from a stationary sequence
- Matched-block bootstrap for dependent data
- Validity of blockwise bootstrap for empirical processes with stationary observations
- Blockwise bootstrapped empirical process for stationary sequences
- Second-order correctness of the blockwise bootstrap for stationary observations
- 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
- Nonlinear time series. Nonparametric and parametric methods
- Large sample confidence regions based on subsamples under minimal assumptions
- On Edgeworth expansion and moving block bootstrap for Studentized \(M\)-estimators in multiple linear regression models
- Normal limits, nonnormal limits, and the bootstrap for quantiles of dependent data
- On the accuracy of bootstrapping sample quantiles of strongly mixing sequences
- Probabilistic Properties of Stochastic Volatility Models
- ON STUDENTIZING AND BLOCKING METHODS FOR IMPLEMENTING THE BOOTSTRAP WITH DEPENDENT DATA
- Bootstrap Confidence Regions Computed from Autoregressions of Arbitrary Order
- On blocking rules for the bootstrap with dependent data
- Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators
- A Smooth Block Bootstrap for Statistical Functionals and Time Series
- A short prehistory of the bootstrap
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