Models for Discrete Longitudinal Data

This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles,...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Molenberghs, Geert (Συγγραφέας), Verbeke, Geert (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: New York, NY : Springer New York, 2005.
Σειρά:Springer Series in Statistics,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Motivating Studies
  • Generalized Linear Models
  • Linear Mixed Models for Gaussian Longitudinal Data
  • Model Families
  • The Strength of Marginal Models
  • Likelihood-based Marginal Models
  • Generalized Estimating Equations
  • Pseudo-Likelihood
  • Fitting Marginal Models with SAS
  • Conditional Models
  • Pseudo-Likehood
  • From Subject-specific to Random-effects Models
  • The Generalized Linear Mixed Model (GLMM)
  • Fitting Generalized Linear Mixed Models with SAS
  • Marginal versus Random-effects Models
  • The Analgesic Trial
  • Ordinal Data
  • The Epilepsy Data
  • Non-linear Models
  • Pseudo-Likelihood for a Hierarchical Model
  • Random-effects Models with Serial Correlation
  • Non-Gaussian Random Effects
  • Joint Continuous and Discrete Responses
  • High-dimensional Joint Models
  • Missing Data Concepts
  • Simple Methods, Direct Likelihood, and Weighted Generalized Estimating Equations
  • Multiple Imputation and the Expectation-Maximization Algorithm
  • Selection Models
  • Pattern-mixture Models
  • Sensitivity Analysis
  • Incomplete Data and SAS.