Theory and Inference for Regression Models with Missing Responses and Covariates.
Theory and Inference for Regression Models with Missing Responses and Covariates.
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具有缺失响应和协变量的回归模型的理论和推理。
DOI：
10.1016/j.jmva.2007.08.009
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发表时间：
2008-07
影响因子：
1.6
通讯作者：
Senchaudhuri, Pralay
中科院分区：
文献类型：
--
作者：
Chen, Qingxia;Ibrahim, Joseph G.;Chen, Ming-Hui;Senchaudhuri, Pralay
在本文中，我们对一般回归模型的缺失响应和协变量数据的推理进行了深入的理论研究。我们假设缺失数据始终为随机缺失 (MAR) 或完全随机缺失 (MCAR)。文献中先前的理论研究仅关注缺失的协变量或缺失的响应，而不是两者。在这里，我们考虑三种不同估计设置下估计的理论属性：完整案例分析（CC）、完整响应分析（CR），其中涉及对仅完全观察到响应的受试者进行分析，以及所有案例分析（AC），这是基于所有案例的分析。在每种情况下，我们都推导出似然度的通用表达式，并基于 EM 算法设计估计方案。我们对正态线性模型中的三种估计方法进行了理论研究，分析了每种方法的信息损失特征，并推导并比较了假设缺失数据为 MAR 或 MCAR 的每种方法的渐近方差。此外，还对CC方法的偏差进行了理论研究。给出了模拟研究和真实数据集来说明该方法。
In this paper, we carry out an in-depth theoretical investigation for inference with missing response and covariate data for general regression models. We assume that the missing data are Missing at Random (MAR) or Missing Completely at Random (MCAR) throughout. Previous theoretical investigations in the literature have focused only on missing covariates or missing responses, but not both. Here, we consider theoretical properties of the estimates under three different estimation settings: complete case analysis (CC), a complete response analysis (CR) that involves an analysis of those subjects with only completely observed responses, and the all case analysis (AC), which is an analysis based on all of the cases. Under each scenario, we derive general expressions for the likelihood and devise estimation schemes based on the EM algorithm. We carry out a theoretical investigation of the three estimation methods in the normal linear model and analytically characterize the loss of information for each method, as well as derive and compare the asymptotic variances for each method assuming the missing data are MAR or MCAR. In addition, a theoretical investigation of bias for the CC method is also carried out. A simulation study and real dataset are given to illustrate the methodology.
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影响因子：
1.9
作者：
Chen, QX;Ibrahim, JG
通讯作者：
Ibrahim, JG
影响因子：
1.9
作者：
Ibrahim, JG;Chen, MH;Lipsitz, SR
通讯作者：
Lipsitz, SR
影响因子：
45.3
作者：
Socinski, MA;Schell, MJ;Kies, MS
通讯作者：
Kies, MS
影响因子：
3.7
作者：
BAKER, SG;LAIRD, NM
通讯作者：
LAIRD, NM
DOI：
10.1198/016214501750332866
发表时间：
2001-03-01
影响因子：
3.7
作者：
Herring, AH;Ibrahim, JG
通讯作者：
Ibrahim, JG