Variance components estimation : mixed models, methodologies and applications
著者
書誌事項
Variance components estimation : mixed models, methodologies and applications
（Monographs on statistics and applied probability, 78）
Chapman & Hall, 1997
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注記
Includes bibliographical references (p. [173]-193) and indexes
内容説明・目次
内容説明
Variance Components Estimation deals with the evaluation of the variation between observable data or classes of data. This is an up-to-date, comprehensive work that is both theoretical and applied. Topics include ML and REML methods of estimation; Steepest-Acent, Newton-Raphson, scoring, and EM algorithms; MINQUE and MIVQUE, confidence intervals for variance components and their ratios; Bayesian approaches and hierarchical models; mixed models for longitudinal data; repeated measures and multivariate observations; as well as non-linear and generalized linear models with random effects.
目次
The Study of Variation. One-Way Classification. Two-Way Cross-Classification. Randomized Blocks. BIBDs and Latin Squares. Nested Classifications. Maximum Likelihood Estimation. The MINQUE and MIVQUE. Non-Negative Estimation of Variance Components. Confidence Intervals. Genetic and Environmental Effects.
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