Missing Observations in Multivariate Regression: Efficiency of a First-Order Method
作者:
H.H. Kelejian,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1969)
卷期:
Volume 64,
issue 328
页码: 1609-1616
ISSN:0162-1459
年代: 1969
DOI:10.1080/01621459.1969.10501080
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
It is shown that the information contained in the incomplete portion of the sample that is relevant in the estimation of the regression parameters is in the form of a linear restriction. Although the parameters of this restriction are generally unknown they can be estimated in terms of the complete portion of the sample. It is then shown that regression parameter estimates based on first order methods, say, Ĉ, differ from the ordinary least squares estimates based only on the complete portion of the sample, say ĈLS, by a function of the extent to which ĈLSfails to satisfy the above named restriction. The asymptotic variance-co-variance matrix of Ĉ is derived under various conditions and compared to that of ĈLS.
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