Semiparametric efficient estimators in heteroscedastic error models
- 주제(키워드) Heteroscedasticity , Semiparametric method , Standardized regression error , Variance function
- 등재 SCIE, SCOPUS
- 발행기관 Springer Tokyo
- 발행년도 2017
- URI http://www.dcollection.net/handler/ewha/000000146807
- 본문언어 영어
- Published As http://dx.doi.org/10.1007/s10463-017-0622-0
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
In the mean regression context, this study considers several frequently encountered heteroscedastic error models 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. This study clarifies the relation between the regression error assumptions and their, respectively, efficiency bounds 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 Cobb–Douglas cost model is the regression mean. © 2017 The Institute of Statistical Mathematics, Tokyo
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