1986/12/01 by C. F. Jeff Wu · 2 citations
Mathematics · Engineering · #Advanced Statistical Methods and Models #Statistical Methods and Inference #Control Systems and Identification #Jackknife resampling #Estimator #Mathematics #Heteroscedasticity #Statistics #Homoscedasticity #Resampling #Nonlinear regression #Ordinary least squares #Robust regression #Extremum estimator #Robustness (evolution) #M-estimator #Regression analysis
paper · doi:10.1214/aos/1176350142
openalex publication_date 1986/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Motivated by a representation for the least squares estimator, we propose a class of weighted jackknife variance estimators for the least squares estimator by deleting any fixed number of observations at a time. They are unbiased for homoscedastic errors and a special case, the delete-one jackknife, is almost unbiased for heteroscedastic errors. The method is extended to cover nonlinear parameters, regression M-estimators, nonlinear regression and generalized linear models. Interval estimators can be constructed from the jackknife histogram. Three bootstrap methods are considered. Two are shown to give biased variance estimators and one does not have the bias-robustness property enjoyed by the weighted delete-one jackknife. A general method for resampling residuals is proposed. It gives variance estimators that are bias-robust. Several bias-reducing estimators are proposed. Some simulation results are reported.