Semiparametric Efficiency in Multivariate Regression Models with Missing Data
作者:
JamesM. Robins,
Andrea Rotnitzky,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 429
页码: 122-129
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476494
出版商: Taylor & Francis Group
关键词: Correlated outcomes;Generalized estimating equations;Generalized least squares;Missing at random;Longitudinal studies
数据来源: Taylor
摘要:
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 onYare 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.
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