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,...
保存先:
| 主要な著者: | , |
|---|---|
| フォーマット: | Livre numérique |
| 言語: | Anglais |
| 出版事項: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| 版: | 1st ed. 2005. |
| シリーズ: | Springer Series in Statistics
|
| オンライン･アクセス: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 注記: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Models for discrete longitudinal data, Geert Molenberghs, [2e édition], New York, NY, Springer, 2010, 1 vol. (XXII, 687 p.), Springer series in statistics, 1-441-92043-9 • Models for Discrete Longitudinal Data, Texte imprimé, 9780387505817 • Models for discrete longitudinal data, Geert Molenberghs, Geert Verbeke, 2005, New York (N.Y.), Springer, 1 vol. (XXII-683 p.), Springer series in statistics, 978-0387-25144-8 |
目次:
- 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.