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Introduction to mixed modelling : beyond regression and analysis of variance/ N. W. Galwey

By: Material type: TextTextPublication details: Chichester: John Wiley, 2014Edition: 2ndDescription: xiii, 487 pages; tables; 25 cmISBN:
  • 9781119945499 (cloth)
Subject(s): DDC classification:
  • 23rd. SA.06 G183
Contents:
1. The need for more than one random-effect term when fitting a regression line -- 2. The need for more than one random-effect term in a designed experiment -- 3. Estimation of the variances of random-effect terms -- 4. Interval estimates for fixed-effect terms in mixed models -- 5. Estimation for random effects in mixed models: Best linear unbiased predictors (BLUPs) -- 6. More advanced mixed models for more elaborate data sets -- 7. Three case studies -- 8. Meta-analysis and the multiple testing problem -- 9. The use of mixed models for the analysis of unbalanced experimental designs -- 10. Beyond mixed modelling -- 11. Why is the criterion for fitting mixed models called Residual maximum likelihood
Summary: This book first introduces the criterion of REstricted Maximum Likelihood (REML) for the fitting of a mixed model to data before illustrating how to apply mixed model analysis to a wide range of situations, how to estimate the variance due to each random-effect term in the model, and how to obtain and interpret Best Linear Unbiased Predictors (BLUPs) estimates of individual effects that take account of their random nature. It is intended to be an introductory guide to a relatively advanced specialised topic, and to convince the reader that mixed modelling is neither so special.
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Includes index.

1. The need for more than one random-effect term when fitting a regression line --
2. The need for more than one random-effect term in a designed experiment --
3. Estimation of the variances of random-effect terms --
4. Interval estimates for fixed-effect terms in mixed models --
5. Estimation for random effects in mixed models: Best linear unbiased predictors (BLUPs) --
6. More advanced mixed models for more elaborate data sets --
7. Three case studies --
8. Meta-analysis and the multiple testing problem --
9. The use of mixed models for the analysis of unbalanced experimental designs --
10. Beyond mixed modelling --
11. Why is the criterion for fitting mixed models called Residual maximum likelihood

This book first introduces the criterion of REstricted Maximum Likelihood (REML) for the fitting of a mixed model to data before illustrating how to apply mixed model analysis to a wide range of situations, how to estimate the variance due to each random-effect term in the model, and how to obtain and interpret Best Linear Unbiased Predictors (BLUPs) estimates of individual effects that take account of their random nature. It is intended to be an introductory guide to a relatively advanced specialised topic, and to convince the reader that mixed modelling is neither so special.

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