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Models for Discrete Longitudinal Data

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Cover of 'Models for Discrete Longitudinal Data'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Motivating Studies
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    Chapter 3 Generalized Linear Models
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    Chapter 4 Linear Mixed Models for Gaussian Longitudinal Data
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    Chapter 5 Model Families
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    Chapter 6 The Strength of Marginal Models
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    Chapter 7 Likelihood-based Marginal Models
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    Chapter 8 Generalized Estimating Equations
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    Chapter 9 Pseudo-Likelihood
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    Chapter 10 Fitting Marginal Models with SAS
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    Chapter 11 Conditional Models
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    Chapter 12 Pseudo-Likehood
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    Chapter 13 From Subject-specific to Random-effects Models
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    Chapter 14 The Generalized Linear Mixed Model (GLMM)
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    Chapter 15 Fitting Generalized Linear Mixed Models with SAS
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    Chapter 16 Marginal versus Random-effects Models
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    Chapter 17 The Analgesic Trial
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    Chapter 18 Ordinal Data
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    Chapter 19 The Epilepsy Data
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    Chapter 20 Non-linear Models
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    Chapter 21 Pseudo-Likelihood for a Hierarchical Model
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    Chapter 22 Random-effects Models with Serial Correlation
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    Chapter 23 Non-Gaussian Random Effects
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    Chapter 24 Joint Continuous and Discrete Responses
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    Chapter 25 High-dimensional Joint Models
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    Chapter 26 Missing Data Concepts
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    Chapter 27 Simple Methods, Direct Likelihood, and Weighted Generalized Estimating Equations
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    Chapter 28 Multiple Imputation and the Expectation-Maximization Algorithm
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    Chapter 29 Selection Models
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    Chapter 30 Pattern-mixture Models
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    Chapter 31 Sensitivity Analysis
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    Chapter 32 Incomplete Data and SAS
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Title
Models for Discrete Longitudinal Data
Published by
Springer New York, January 2005
DOI 10.1007/0-387-28980-1
ISBNs
978-0-387-28980-9, 978-0-387-25144-8
Authors

Molenberghs, Geert, Verbeke, Geert

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 73 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 26%
Student > Master 12 16%
Researcher 9 12%
Professor 6 8%
Professor > Associate Professor 5 7%
Other 13 18%
Unknown 10 14%
Readers by discipline Count As %
Medicine and Dentistry 12 16%
Mathematics 12 16%
Social Sciences 6 8%
Agricultural and Biological Sciences 5 7%
Psychology 5 7%
Other 19 26%
Unknown 15 20%