Variance components
Author(s)
Bibliographic Information
Variance components
（Wiley series in probability and mathematical statistics, . Applied probability and statistics）
Wiley, c1992
Available at / 48 libraries
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417.6-Se11923001280,10092300916,10092300917,10092302327,10092302331
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Note
"A Wiley-Interscience publication."
Includes bibliographical references (p. 475-489) and indexes
Description and Table of Contents
Description
This text presents a broad coverage of variance components. It deals with the estimation of variance components and the prediction of realized but unobservable values of random variables in analysis of variance models and in binary and discrete data. The authors begin with an introduction to the subject, which details more complicated types of data appearing in subsequent chapters. All the major methods of estimating components are discussed at length, including ANOVA, ML, REML, and Bayes. Topics covered include history, analysis of variance estimation, maximum likelihood (ML) estimation, prediction in mixed models, Bayes estimation and hierarchical models, categorical data, covariance components and minimum norm estimation, dispersion-mean model, kurtosis and fourth moments.
Table of Contents
- History and comment
- the 1-way classification
- balanced data
- analysis of variance estimation for unbalanced data
- maximum likelihood (ML) and restricted maximum likelihood (REML)
- prediction of random variables
- computing ML and REML estimates
- hierarchical models and Bayesian estimation
- binary and discrete data
- other procedures
- the dispersion-mean model.
by "Nielsen BookData"
