On resampling methods for variance and bias estimation in linear models

DOI10.1214/aos/1176350945zbMath0651.62063OpenAlexW2009586519MaRDI QIDQ1106593

Jun Shao

Publication date: 1988

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176350945

zbMATH Keywords

bootstrapleast squares estimatorBias reductionresampling methodsConsistencymean squared errorsheteroscedastic linear modellarge sample propertiesimbalance measureasymptotically unbiasedweighted jackknifebias estimatorshomoscedastic error model.resampling variance

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Robustness and adaptive procedures (parametric inference) (62F35) Nonparametric inference (62G99)

Related Items (7)

Consistency of least-squares estimator and its jackknife variance estimator in nonlinear models ⋮ Wild bootstrap estimation in partially linear models with heteroscedasticity ⋮ Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment ⋮ A quadratic approximation for jackknife estimators of the variance of sample mean functions ⋮ An alternative approach to obtaining Nagar-type moment approximations in simultaneous equation models ⋮ Bootstrap methods for heteroskedastic regression models: evidence on estimation and testing ⋮ Jackknifing type weighted least squares estimators in partially linear regression models.

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