SEMIPARAMETRIC EFFICIENCY IN MULTIVARIATE REGRESSION-MODELS WITH MISSING DATA
SEMIPARAMETRIC EFFICIENCY IN MULTIVARIATE REGRESSION-MODELS WITH MISSING DATA
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DOI：
10.2307/2291135
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发表时间：
1995-03-01
影响因子：
3.7
通讯作者：
ROTNITZKY, A
中科院分区：
文献类型：
--
作者：
ROBINS, JM;ROTNITZKY, A
我们考虑了当Y上的数据随机缺失时，半参数模型的参数估计的效率界仅由相关结果向量Y的均值的限制来定义。我们证明了半参数方差界是截尾加权估计逆概率中最优估计的渐近方差界，并且当数据完全随机缺失时，这个界是不变的。对于这种情况，我们研究了均值参数的广义估计方程(GEE)估计的渐近性能，证明了除特殊情况外，最优GEE估计是无效的。最优加权估计依赖于未知的总体数量。但是对于单调缺失数据，我们提出了一种自适应估计量，它的渐近方差可以达到这个界。
We consider the efficiency bound for the estimation of the parameters of semiparametric models defined solely by restrictions on the means of a vector of correlated outcomes, Y, when the data on Y are missing at random. We show that the semiparametric variance bound is the asymptotic variance of the optimal estimator in a class of inverse probability of censoring weighted estimators and that this bound is unchanged if the data are missing completely at random. For this case we study the asymptotic performance of the generalized estimating equations (GEE) estimators of mean parameters and show that the optimal GEE estimator is inefficient except for special cases. The optimal weighted estimator depends on unknown population quantities. But for monotone missing data, we propose an adaptive estimator whose asymptotic variance can achieve the bound.