Optimal Unbiased Estimation of Variance Components (Paperback, Softcover reprint of the original 1st ed. 1986)

The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates for variance components in a mixed linear model with unbalanced data was considered a miasma of competing, generally weakly motivated estimators, with few firm gUidelines and many simple, compelling but Unanswered questions. Then in 1971 two significant beachheads were secured: the results of Rao [1971a, 1971b] and his MINQUE estimators, and related to these but not originally derived from them, the results of Seely [1971] obtained as part of his introduction of the no~ion of quad- ratic subspace into the literature of variance component estimation. These two approaches were ultimately shown to be intimately related by Pukelsheim [1976], who used a linear model for the com- ponents given by Mitra [1970], and in so doing, provided a mathemati- cal framework for estimation which permitted the immediate applica- tion of many of the familiar Gauss-Markov results, methods which had earlier been so successful in the estimation of the parameters in a linear model with only fixed effects. Moreover, this usually enor- mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation tech- niques, thereby unifying the subject in addition to generating answers.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Lecture Notes in Statistics, 39
Release date:	December 1986
First published:	November 1986
Authors:	James D Malley
Dimensions:	244 x 170 x 8mm (L x W x T)
Format:	Paperback
Pages:	146
Edition:	Softcover reprint of the original 1st ed. 1986
ISBN-13:	978-0-387-96449-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Promotions
LSN:	0-387-96449-5
Barcode:	9780387964492

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