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,...
محفوظ في:
| المؤلفون الرئيسيون: | , |
|---|---|
| التنسيق: | Livre numérique |
| اللغة: | Anglais |
| منشور في: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| الطبعة: | 1st ed. 2005. |
| سلاسل: | Springer Series in Statistics
|
| الوصول للمادة أونلاين: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| ملاحظة: |
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 |
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| 100 | 1 | |a Molenberghs, Geert. | |
| 245 | 1 | 0 | |a Models for Discrete Longitudinal Data |c by Geert Molenberghs, Geert Verbeke. |
| 250 | |a 1st ed. 2005. | ||
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Springer Series in Statistics |x 2197-568X | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 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. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001-2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005-2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and received the American Statistical Association's Excellence in Continuing Education Award, based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004. . | ||
| 700 | 1 | |a Verbeke, Geert. |4 aut | |
| 776 | 0 | |0 169647749 |t Models for discrete longitudinal data |f Geert Molenberghs |e [2e édition] |c New York, NY |n Springer |d 2010 |p 1 vol. (XXII, 687 p.) |s Springer series in statistics |z 1-441-92043-9 | |
| 776 | 0 | |t Models for Discrete Longitudinal Data |b Texte imprimé |z 9780387505817 | |
| 776 | 0 | |0 09482102X |t Models for discrete longitudinal data |f Geert Molenberghs, Geert Verbeke |d 2005 |c New York (N.Y.) |n Springer |p 1 vol. (XXII-683 p.) |s Springer series in statistics |z 978-0387-25144-8 | |
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