Semiparametric efficient estimators in heteroscedastic error models

Mijeong Kim, Yanyuan Ma

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalAnnals of the Institute of Statistical Mathematics
Volume71
Issue number1
DOIs
StatePublished - Feb 1 2019

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Heteroscedastic Errors
Heteroscedastic Model
Efficient Estimator
Error Model
Regression
Variance Function
Cost Model
Data analysis
Estimator
Series

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

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Semiparametric efficient estimators in heteroscedastic error models. / Kim, Mijeong; Ma, Yanyuan.

In: Annals of the Institute of Statistical Mathematics, Vol. 71, No. 1, 01.02.2019.

Research output: Contribution to journalArticle

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