By Hardeo Sahai

ISBN-10: 0817632298

ISBN-13: 9780817632298

Systematic remedy of the generally hired crossed and nested class types utilized in research of variance designs with an in depth and thorough dialogue of convinced random results types no longer normally present in texts on the introductory or intermediate point. it's also numerical examples to investigate information from a wide selection of disciplines in addition to any labored examples containing computing device outputs from commonplace software program programs corresponding to SAS, SPSS, and BMDP for every numerical instance.

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Additional resources for Analysis of Variance for Random Models, Volume 2: Unbalanced Data: Theory, Methods, Applications, and Data Analysis

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R. L. Anderson (1961), Designs for estimating variance components, in Proceedings of the Seventh Conference on Design of Experiments and Army Preservation and Development Testing, 781–823; also published as Mim. Ser. 310, Institute of Statistics, North Carolina State University, Raleigh, NC. R. L. Anderson and P. P. Crump (1967), Comparisons of designs and estimation procedures for estimating parameters in a two-stage nested process, Technometrics, 9, 499–516. T. R. Bainbridge (1963), Staggered nested designs for estimating variance components, in American Society for Quality Control Annual Conference Transactions, American Society for Quality Control, Milwaukee, 93–103.

X H −1 X ⎤ ⎥ ⎥ ⎥. 6), respectively, has full row rank; and from the relation T X = 0 it follows that the rows of T are linearly independent of those of X . 11), it can be readily seen that the log-likelihood of Y ∗ is the sum of the log-likelihoods of T Y and X H −1 Y . 8. Restricted Maximum Likelihood Estimation − 1 (Y − Xα) H −1 X(X H −1 X)−1 X H −1 (Y − Xα). 13) Now, L1 does not involve α; so that the REML estimators of σe2 and the variance ratios ρi s contained in H are those values of σe2 and ρi s that maximize L1 subject to the constraints that σe2 and ρi s are nonnegative.

P. 4) i=1 a linear combination of the variance components σi2 s, by a quadratic form Y AY , where A is a symmetric matrix chosen subject to the conditions which guarantee the estimator’s unbiasedness and invariance to changes in α. For unbiasedness, we must have p E(Y AY ) = 2 i σi . 3), becomes p E(Y AY ) = α X AXα + σi2 tr[AVi ]. 5) is equivalent to p α X AXα + p σi2 tr[AVi ] i=1 = 2 i σi . i=1 Thus the estimator Y AY is unbiased if and only if A is chosen to satisfy X AX = 0 and tr[AVi ] = i.

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Analysis of Variance for Random Models, Volume 2: Unbalanced Data: Theory, Methods, Applications, and Data Analysis by Hardeo Sahai

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