SEMIPARAMETRIC OPTIMAL ESTIMATION WITH NONIGNORABLE NONRESPONSE DATA

• 成果类型: Article
• 署名作者: Morikawa, Kosuke; Kim, Jae Kwang
• 署名单位: University of Osaka; Iowa State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/21-AOS2070
• 发表日期: 2021
• 页码: 2991-3014
• 关键词: GOODNESS-OF-FIT; sensitivity analysis; regression-models; Missing Data; drop-out; imputation; INDEPENDENCE; inference

摘要

When the response mechanism is believed to be not missing at random (NMAR), a valid analysis requires stronger assumptions on the response mechanism than standard statistical methods would otherwise require. Semiparametric estimators have been developed under the parametric model assumptions on the response mechanism. In this paper, a new statistical test is proposed to guarantee model identifiability without using instrumental variable assumption. Furthermore, we develop optimal semiparametric estimation for parameters such as the population mean. Specifically, we propose two semiparametric optimal estimators that do not require any model assumptions other than the response mechanism. Asymptotic properties of the proposed estimators are discussed. An extensive simulation study is presented to compare with some existing methods. We present an application of our method using Korean labor and income panel survey data.