Finite-sample performance of the robust variance estimator in the presence of missing data
Finite-sample performance of the robust variance estimator in the presence of missing data
复制标题
存在缺失数据时鲁棒方差估计器的有限样本性能
DOI：
10.1080/03610918.2022.2084107
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Gosho Masahiko
中科院分区：
文献类型：
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作者：
Ishii Ryota;Maruo Kazushi;Doi Masaaki;Gosho Masahiko
理论上，模型错配下的极大似然估计量具有三明治型渐近方差-协方差矩阵。它的经验估计量，即稳健方差估计量，是一致的。因此，即使在模型不规范的情况下，估计量也是渐近有效的。在实际应用中，在纵向数据分析中使用稳健方差估计量来计算标准误差。最近，Golden等人（Econometrics, 7,1 -27）表明，在存在缺失数据的情况下，即使缺失数据的机制不是随机缺失，最大似然估计量也保持三明治型渐近方差-协方差矩阵。虽然他们揭示了在同时存在模型错误规范和缺失数据的情况下稳健方差估计器的渐近有效性，但其有限样本性能并未进行调查。在本文中，我们通过模拟研究评估了有限样本性能，并澄清了其小样本问题。此外，我们使用随机双盲研究的纵向CD4计数数据说明了稳健方差估计。
Theoretically, the maximum likelihood estimator has the sandwich-type asymptotic variance-covariance matrix under model misspecification. Its empirical estimator, that is called the robust variance estimator, is consistent. Thus, the estimator is asymptotically valid even under model misspecification. In practice, the robust variance estimator is used for computation of standard errors in longitudinal data analysis. Recently, Golden et al. ( Econometrics, 7, 1-27) showed that the maximum likelihood estimator retains a sandwich-type asymptotic variance-covariance matrix in the presence of missing data even when the missing-data mechanism is missing not at random. Although they revealed the asymptotic validity of the robust variance estimator in the simultaneous presence of both model misspecification and missing data, its finite-sample performance did not be investigated. In this article, we evaluated the finite-sample performance via simulation studies and clarify its small-sample problems. In addition, we illustrated the robust variance estimator using longitudinal CD4 count data from a randomized double-blind study.