Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model.
Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model.
复制标题
非单身酮丢失 - 非狂热数据的半参数推断：无自审查模型。
DOI：
10.1080/01621459.2020.1862669
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发表时间：
2022
影响因子：
3.7
通讯作者：
Tchetgen, Eric J. Tchetgen
中科院分区：
文献类型：
--
作者：
Malinsky, Daniel;Shpitser, Ilya;Tchetgen, Eric J. Tchetgen
我们研究了非单调和非随机缺失的多元数据的统计泛函的识别和估计，采用半参数方法。具体来说，我们假设缺失机制满足以前所谓的“无自删失”或“逐项条件独立无响应”，这大致对应于没有部分观察变量直接决定其自身缺失状态的假设。我们表明，这一假设，结合比值比参数化的联合密度，使识别感兴趣的泛函，我们建立了半参数效率界的非参数模型满足这一假设。我们提出了一个实用的增广逆概率加权估计，并在设置（可能是高维）始终观察到的协变量的子集，我们提出的估计享有一定的双重鲁棒性。我们探索我们的估计与模拟实验的性能和先前研究的数据集在博茨瓦纳的艾滋病毒阳性母亲。
We study the identification and estimation of statistical functionals of multivariate data missing non-monotonically and not-at-random, taking a semiparametric approach. Specifically, we assume that the missingness mechanism satisfies what has been previously called “no self-censoring” or “itemwise conditionally independent nonresponse,” which roughly corresponds to the assumption that no partially-observed variable directly determines its own missingness status. We show that this assumption, combined with an odds ratio parameterization of the joint density, enables identification of functionals of interest, and we establish the semiparametric efficiency bound for the nonparametric model satisfying this assumption. We propose a practical augmented inverse probability weighted estimator, and in the setting with a (possibly high-dimensional) always-observed subset of covariates, our proposed estimator enjoys a certain double-robustness property. We explore the performance of our estimator with simulation experiments and on a previously-studied data set of HIV-positive mothers in Botswana.
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影响因子：
2.3
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1080/01621459.2016.1224713
发表时间：
2017-06-01
影响因子：
3.7
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
NEWEY, WK
影响因子：
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作者：
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通讯作者：
Tchetgen, Eric J. Tchetgen