Subsample ignorable likelihood for regression analysis with missing data
Subsample ignorable likelihood for regression analysis with missing data
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DOI：
10.1111/j.1467-9876.2011.00763.x
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发表时间：
2011-01-01
影响因子：
1.6
通讯作者：
Zhang, Nanhua
中科院分区：
文献类型：
--
作者：
Little, Roderick J.;Zhang, Nanhua
缺失协变量回归的两种常见方法是完全案例分析和可重复似然方法。我们回顾这些方法，并提出了一个混合类，称为子样本似然方法，它适用于子样本的观测，是完整的一组变量，但可能不完整的其他的似然方法。缺失数据机制的条件下，子样本可重复似然给出一致的估计，但完整的情况下分析和可重复似然方法是不一致的。我们激励和应用所提出的方法从全国健康和营养检查调查的数据，我们通过模拟说明的方法的属性。扩展到非可能性分析也提到。
Two common approaches to regression with missing covariates are complete-case analysis and ignorable likelihood methods. We review these approaches and propose a hybrid class, called subsample ignorable likelihood methods, which applies an ignorable likelihood method to the subsample of observations that are complete on one set of variables, but possibly incomplete on others. Conditions on the missing data mechanism are presented under which subsample ignorable likelihood gives consistent estimates, but both complete-case analysis and ignorable likelihood methods are inconsistent. We motivate and apply the method proposed to data from the National Health and Nutrition Examination Survey, and we illustrate properties of the methods by simulation. Extensions to non-likelihood analyses are also mentioned.