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Title: Semiparametric efficient estimators in heteroscedastic error models Authors:  Mijeong Kim - Ewha Womans University (Korea, South) [presenting]
Yanyuan Ma - Pennsylvania State University (United States)
Abstract: In the mean regression context, several frequently encountered heteroscedastic error models are considered where the regression mean and variance functions are specified up to certain parameters. An important point we note through a series of analyses is that different assumptions on standardized regression errors yield quite different efficiency bounds for the corresponding estimators. Consequently, all aspects of the assumptions need to be specifically taken into account in constructing their corresponding efficient estimators. The relation between the regression error assumptions and their respectively efficiency bounds is clarified under the general regression framework with heteroscedastic errors. Our simulation results support our findings; we carry out a real data analysis using the proposed methods where the CobbDouglas cost model is the regression mean.