Effect of Violation of the Normal Assumption on MI and ML Estimators in the Analysis of Incomplete Data
Effect of Violation of the Normal Assumption on MI and ML Estimators in the Analysis of Incomplete Data
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DOI：
10.1080/03610926.2013.819920
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
S. Hojo;Michio Yamamoto;Y. Kano
中科院分区：
文献类型：
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作者：
S. Hojo;Michio Yamamoto;Y. Kano
在具有 MAR 缺失数据的一般分布下，在简单回归模型中研究基于正态理论的 ML/MI 估计量的渐近分布。残差方差的 ML/MI 估计量的渐近方差是明确导出的，由此可知误差分布的峰度主要影响渐近方差。为了研究估计器的有限样本特性而进行的数值模拟结果在很大程度上符合渐近结果，并且它们还表明了有趣的发现，特别是对于小样本，这些发现并不遵循渐近特性。结论是，ML 估计器在此研究的情况下表现最佳。
Asymptotic distributions of normal-theory-based ML/MI estimators are studied in a simple regression model under general distributions with MAR missing data. The asymptotic variance of the ML/MI estimator of residuals’ variance is explicitly derived, from which it follows that the kurtosis of the error distribution primarily affects the asymptotic variance. Results of numerical simulations conducted to study finite sample properties of the estimators, conformed largely to the asymptotic results, and they also indicated interesting findings particularly for small samples, which do not follow from the asymptotic property. It is concluded that the ML estimators perform best in the situation studied here.