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Correlated data analysis : modeling, analytics, and applications / Peter X.-K. Song.

By: Song, Peter X.-K (Xue-Kun), 1964-.
Material type: TextTextSeries: Springer series in statistics. Publisher: New York : Springer, c2007Description: xv, 346 p. : ill. ; 24 cm.ISBN: 9780387713922 (acidfree paper); 0387713921 (acidfree paper); 9780387713939 (e-ISBN).Subject(s): Correlation (Statistics) | Generalized estimating equationsDDC classification: 519.537 Online resources: WorldCat details | E-book Fulltext
Contents:
Table of contents 1. Introduction and examples -- 2. Dispersion models -- 3. Inference functions -- 4. Modeling correlated data -- 5. Marginal generalized linear models -- 6. Vector generalized linear models -- 7. Mixed-effects models: likelihood-based inference -- 8. Mixed-effects models: Bayesian inference -- 9. Linear predictors -- 10. Generalized state space models -- 11. Generalized state space models for longitudinal binomial data -- 12. Generalized state space models for longitudinal count data -- 13. Missing data in longitudinal studie
Summary: Summary: This book covers recent developments in correlated data analysis, using the class of dispersion models as marginal components in the formulation of joint models for correlated data. Much new material Read more...
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Non-fiction 519.537 SOC 2007 (Browse shelf) Not for loan
Text Text EWU Library
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Non-fiction 519.537 SOC 2007 (Browse shelf) C-1 Not For Loan 26630
Text Text EWU Library
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Non-fiction 519.537 SOC 2007 (Browse shelf) C-3 Available 26891
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Includes bibliographical references and index.

Table of contents 1. Introduction and examples --
2. Dispersion models --
3. Inference functions --
4. Modeling correlated data --
5. Marginal generalized linear models --
6. Vector generalized linear models --
7. Mixed-effects models: likelihood-based inference --
8. Mixed-effects models: Bayesian inference --
9. Linear predictors --
10. Generalized state space models --
11. Generalized state space models for longitudinal binomial data --
12. Generalized state space models for longitudinal count data --
13. Missing data in longitudinal studie

Summary:
This book covers recent developments in correlated data analysis, using the class of dispersion models as marginal components in the formulation of joint models for correlated data. Much new material Read more...

Applied Statistics

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