Statistical inference with semiparametric nonignorable nonresponse models
Statistical inference with semiparametric nonignorable nonresponse models
复制标题
DOI：
10.1111/sjos.12652
复制
发表时间：
2023-04
影响因子：
1
通讯作者：
Masatoshi Uehara;Danhyang Lee;Jae Kwang Kim
中科院分区：
文献类型：
--
作者：
Masatoshi Uehara;Danhyang Lee;Jae Kwang Kim
在缺失数据的统计分析中，如何处理不可验证的响应往往是一个具有挑战性的问题。响应机制的参数模型假设对模型误设定敏感。我们考虑一个半参数响应模型，放松了响应机制中的参数模型假设。本文提出了两类有效的估计量：剖面极大似然估计量和剖面校正估计量，并研究了它们的渐近性质。两个广泛的模拟研究与现有的一些方法进行比较。我们提出了一个应用程序，我们的方法使用的数据从韩国劳动和收入面板调查。
How to deal with nonignorable response is often a challenging problem encountered in statistical analysis with missing data. Parametric model assumption for the response mechanism is sensitive to model misspecification. We consider a semiparametric response model that relaxes the parametric model assumption in the response mechanism. Two types of efficient estimators, profile maximum likelihood estimator and profile calibration estimator, are proposed, and their asymptotic properties are investigated. Two extensive simulation studies are used to compare with some existing methods. We present an application of our method using data from the Korean Labor and Income Panel Survey.