Paper Publications

Hits:

Affiliation of Author(s):数学与统计学院

Teaching and Research Group:统计教研室

Journal:Journal of Multivariate Analysis

Funded by:其他课题

Key Words:Conditional moment restrictions,Combined empirical likelihood,Missing response,Unconditional moment restrictions,Wilks’ theorem

Abstract:Many statistical models, e.g. regression models, can be viewed as conditional moment restrictions when distributional assumptions on the error term are not assumed. For suchmodels, several estimators that achieve the semiparametric efficiency bound have beenproposed. However, in many studies, auxiliary information is available as unconditional moment restrictions. Meanwhile, we also consider the presence of missing responses. We propose the combined empirical likelihood (CEL) estimator to incorporate such auxiliary information to improve the estimation efficiency of the conditional moment restriction models.We show that, when assuming responses are strongly ignorable missing at random, the CEL estimator achieves better efficiency than the previous estimators due to utilization of the auxiliary information. Based on the asymptotic property of the CEL estimator,we also develop Wilks’ type tests and corresponding confidence regions for the model parameter and the mean response. Since kernel smoothing is used, the CEL method may have difficulty for problems with high dimensional covariates. In such situations, we propose an instrumental variable-based empirical likelihood (IVEL) method to handle this problem. The merit of the CEL and IVEL are further illustrated through simulation studies.

Co-author:刘天庆,林楠,张宝学

First Author:Yuan Xiaohui

Indexed by:Journal paper

Issue:101

Page Number:1

ISSN No.:0047-259X

Translation or Not:no

Date of Publication:2010-10-01