Correlated data analysis : modeling, analytics, and applications (Book, 2007) [WorldCat.org]
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Correlated data analysis : modeling, analytics, and applications

Author: Peter X -K Song
Publisher: New York : Springer, ©2007.
Series: Springer series in statistics.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"This book presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models." "Various real-world data examples, numerical illustrations and software usage tips are presented  Read more...
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Details

Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Peter X -K Song
ISBN: 9780387713922 0387713921 9780387713939 038771393X 144192440X 9781441924407
OCLC Number: 149011056
Description: xv, 346 : illustrations ; 24 cm
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 studies.
Series Title: Springer series in statistics.
Responsibility: Peter X.-K. Song.
More information:

Abstract:

It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. In addition to the discussions on marginal models and mixed-effects models,  Read more...

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From the reviews:"The book presents recent developments in the field of correlated data analysis. Its aim is to give a systematic account of regression models and their application to the modelling Read more...

 
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