A weighted estimating equation for missing covariate data with properties similar to maximum likelihood
A weighted estimating equation for missing covariate data with properties similar to maximum likelihood
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DOI：
10.2307/2669931
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发表时间：
1999-12-01
影响因子：
3.7
通讯作者：
Zhao, LP
中科院分区：
文献类型：
--
作者：
Lipsitz, SR;Ibrahim, JG;Zhao, LP
在回归分析中，经常会出现协变量数据缺失的情况。最近分析此类数据的方法是加权估计方程。通过加权估计方程，完整观测对估计方程的贡献通过被观测的逆概率进行加权。在本文中，我们提出了一个与最大似然估计方程几乎相同的加权估计方程。因此，我们提出了一种 EM 型算法来求解这些加权估计方程。尽管加权估计方程是 Robins 等人早期提出的方程的特例，但我们用于求解它们的 EM 型算法是新的。与 Robins 和 Ritov 类似，我们给出的结果是，为了获得回归参数的一致估计，必须正确指定缺失数据机制或给定观测数据的缺失数据的分布。我们通过两个例子（模拟和渐近研究）将加权估计方程与最大似然进行比较。
In regression analysis, missing covariate data occurs often. A recent approach to analyzing such data is weighted estimating equations. With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse probability of being observed. In this article we propose a weighted estimating equation that is almost identical to the maximum likelihood estimating equations. As such, we propose an EM-type algorithm to solve these weighted estimating equations. Although the weighted estimating equations are a special case of those proposed earlier by Robins et al., our EM-type algorithm to solve them is new. Similar to Robins and Ritov, we give the result that to obtain a consistent estimate of the regression parameters, either the missing-data mechanism or the distribution of thr missing data given the observed data must be correctly specified. We compare the weighted estimating equations to maximum likelihood via two examples, a simulation and an asymptotic study.