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Correlated data analysis : modeling, analytics, and applications
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 generali...
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| Auteur principal : | |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Correlated data analysis : modeling, analytics, and applications / Peter X.-K. Song |
| Édition : | 1st ed. 2007. |
| Publié : |
New York, NY :
Springer New York
, [20..] Cham : Springer Nature |
| Collection : | Springer series in statistics (Internet) |
| Accès en ligne : |
Accès Nantes Université
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| Note sur l'URL : | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex |
| Condition d'utilisation et de reproduction : | Conditions particulières de réutilisation pour les bénéficiaires des licences nationales : https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |
| Contenu : | and Examples. Dispersion Models. Inference Functions. Modeling Correlated Data. Marginal Generalized Linear Models. Vector Generalized Linear Models. Mixed-Effects Models: Likelihood-Based Inference. Mixed-Effects Models: Bayesian Inference. Linear Predictors. Generalized State Space Models. Generalized State Space Models for Longitudinal Binomial Data. Generalized State Space Models for Longitudinal Count Data. Missing Data in Longitudinal Studies |
| Sujets : | |
| Documents associés : | Autre format:
Correlated Data Analysis: Modeling, Analytics, and Applications Autre format: Correlated Data Analysis: Modeling, Analytics, and Applications Autre format: Correlated data analysis |
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| 200 | 1 | |a Correlated data analysis |e modeling, analytics, and applications |f Peter X.-K. Song | |
| 205 | |a 1st ed. 2007. | ||
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| 327 | 1 | |a and Examples |a Dispersion Models |a Inference Functions |a Modeling Correlated Data |a Marginal Generalized Linear Models |a Vector Generalized Linear Models |a Mixed-Effects Models: Likelihood-Based Inference |a Mixed-Effects Models: Bayesian Inference |a Linear Predictors |a Generalized State Space Models |a Generalized State Space Models for Longitudinal Binomial Data |a Generalized State Space Models for Longitudinal Count Data |a Missing Data in Longitudinal Studies | |
| 330 | |a 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. One example is correlated angular data. This book provides a systematic treatment for the topic of estimating functions. Under this framework, both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to marginal models and mixed-effects models, this book covers topics on joint regression analysis based on Gaussian copulas and generalized state space models for longitudinal data from long time series. Various real-world data examples, numerical illustrations and software usage tips are presented throughout the book. This book has evolved from lecture notes on longitudinal data analysis, and may be considered suitable as a textbook for a graduate course on correlated data analysis. This book is inclined more towards technical details regarding the underlying theory and methodology used in software-based applications. Therefore, the book will serve as a useful reference for those who want theoretical explanations to puzzles arising from data analyses or deeper understanding of underlying theory related to analyses. Peter Song is Professor of Statistics in the Department of Statistics and Actuarial Science at the University of Waterloo. Professor Song has published various papers on the theory and modeling of correlated data analysis. He has held a visiting position at the University of Michigan School of Public Health (Ann Arbor, Michigan) | ||
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