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Semiparametric efficient estimators in heteroscedastic error models

Title
Semiparametric efficient estimators in heteroscedastic error models
Authors
Kim M.Ma Y.
Ewha Authors
김미정
SCOPUS Author ID
김미정scopus
Issue Date
2017
Journal Title
Annals of the Institute of Statistical Mathematics
ISSN
0020-3157JCR Link
Citation
pp. 1 - 28
Keywords
HeteroscedasticitySemiparametric methodStandardized regression errorVariance function
Publisher
Springer Tokyo
Indexed
SCIE; SCOPUS scopus
Abstract
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
DOI
10.1007/s10463-017-0622-0
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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