TY - JOUR
T1 - Semiparametric efficient estimators in heteroscedastic error models
AU - Kim, Mijeong
AU - Ma, Yanyuan
N1 - Publisher Copyright:
© 2017, The Institute of Statistical Mathematics, Tokyo.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - 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.
AB - 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.
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U2 - 10.1007/s10463-017-0622-0
DO - 10.1007/s10463-017-0622-0
M3 - Article
AN - SCOPUS:85031400063
SN - 0020-3157
VL - 71
SP - 1
EP - 28
JO - Annals of the Institute of Statistical Mathematics
JF - Annals of the Institute of Statistical Mathematics
IS - 1
ER -