文章基本信息

标题：On Decomposing the Watson Efficiency of Ordinary Least Squares in a Partitioned Weakly Singular Linear Model
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作者：K.L. Chu ; McGill University, Montr\'eal, Canada ; J. Isotalo 等
期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2004
卷号：66
期号：04
出版社：Indian Statistical Institute
摘要：We consider the estimation of regression coefficients in a partitioned weakly singular linear model and focus on questions concerning the Watson efficiency of the ordinary least squares estimator of a subset of the parameters with respect to the best linear unbiased estimator. Certain submodels are also considered. The conditions under which the Watson efficiency in the full model splits into a function of some other Watson efficiencies is given special attention. In particular, a new decomposition of the Watson efficiency into a product of three particular factors appears to be very useful.
关键词：Best linear unbiased estimation, Bloomfield-Watson-Knott inequality, BLUE, BWK inequality, efficiency multiplier, FWL theorem, Frisch-Waugh-Lovell theorem, Gauss-Markov model, OLSE, ordinary least squares, partitioned linear model, reduced linear model, splitting the efficiency, total Watson efficiency, weakly singular linear model, Zyskind-Martin model.