Objective: To solve the problem of parameter estimate in the regression analysis of non-random sample. Methods: Calculating residuals according to the regression function based on original data. Modifying residuals an...Objective: To solve the problem of parameter estimate in the regression analysis of non-random sample. Methods: Calculating residuals according to the regression function based on original data. Modifying residuals and correcting them with mean. Adding mean-corrected residuals on original response and bootstrapping them to get 1000 samples. Fitting regression functions of 1000 resampling samples and calculating the 2.5th percentile and 97.5th percentile of corresponding coefficient. Results: The interval estimates deriving from bootstrap method had more statistical significance than that from usual method. Conclusion: Bootstrapping a regression with residuals is a valid method for estimating parameter in regression analysis.展开更多
文摘Objective: To solve the problem of parameter estimate in the regression analysis of non-random sample. Methods: Calculating residuals according to the regression function based on original data. Modifying residuals and correcting them with mean. Adding mean-corrected residuals on original response and bootstrapping them to get 1000 samples. Fitting regression functions of 1000 resampling samples and calculating the 2.5th percentile and 97.5th percentile of corresponding coefficient. Results: The interval estimates deriving from bootstrap method had more statistical significance than that from usual method. Conclusion: Bootstrapping a regression with residuals is a valid method for estimating parameter in regression analysis.
