On Edgeworth expansion and moving block bootstrap for Studentized \(M\)-estimators in multiple linear regression models

DOI10.1006/jmva.1996.0003zbMath0864.62028OpenAlexW2016756911MaRDI QIDQ1907831

Soumendra Nath Lahiri

Publication date: 22 June 1997

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1996.0003

zbMATH Keywords

stationarity\(M\)-estimatorstrong mixingmoving block bootstrapmultiple linear regression modeltwo-term Edgeworth expansionStudentized multivariate \(M\)-estimator

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Nonparametric statistical resampling methods (62G09)

Related Items

Bootstraps for time series, Bootstrapping GMM estimators for time series, Consistency of the jackknife-after-bootstrap variance estimator for the bootstrap quantiles of a Studentized statistic, Edgeworth expansions for Studentized statistics under weak dependence, Asymptotic expansions for sums of block-variables under weak dependence, On inconsistency of the jackknife-after bootstrap bias estimator for dependent data, Bootstrap Methods for Time Series, Fixed-smoothing asymptotics for time series, A higher-order correct fast moving-average bootstrap for dependent data, Second order correctness of perturbation bootstrap M-estimator of multiple linear regression parameter, Consistency of a hybrid block bootstrap for distribution and variance estimation for sample quantiles of weakly dependent sequences, Inference functions and quadratic score tests, A bootstrap procedure in linear regression with nonstationary errors, A nonparametric plug-in rule for selecting optimal block lengths for block bootstrap methods, BLOCK BOOTSTRAP HAC ROBUST TESTS: THE SOPHISTICATION OF THE NAIVE BOOTSTRAP, A short prehistory of the bootstrap, Discussion on: ``Bootstrap methods for dependent data: a review, Improved nonparametric confidence intervals in time series regressions, Uncertainty quantification in robust inference for irregularly spaced spatial data using block bootstrap, Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models, The Block-Block Bootstrap for Time Series, Moving Block Bootstrap for Analyzing Longitudinal Data, On the accuracy of bootstrapping sample quantiles of strongly mixing sequences, On Mixture Periodic Vector Autoregressive Models