Semiparametric optimal estimation with nonignorable nonresponse data
Semiparametric optimal estimation with nonignorable nonresponse data
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DOI：
10.1214/21-aos2070
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Kosuke Morikawa;Jae Kwang Kim
中科院分区：
文献类型：
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作者：
Kosuke Morikawa;Jae Kwang Kim
当响应机制被认为不是随机缺失（NMAR）时，有效的分析需要比标准统计方法更强的响应机制假设。半参数估计已开发的模型假设下的响应机制。本文提出了一种新的统计检验方法，在不使用任何工具变量的情况下保证模型的可辨识性。此外，我们开发了最优半参数估计的参数，如人口的平均值。具体来说，我们提出了两个半参数最优估计，不需要任何模型假设以外的响应机制。估计的渐近性质进行了讨论。一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，我们的方法使用韩国劳动和收入面板调查数据。
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 model assumptions on the response mechanism. In this paper, a new statistical test is proposed to guarantee model identifiability without using any instrumental variable. 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.