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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| Main Author: | |
|---|---|
| Format: | Livre numérique |
| Language: | Anglais |
| Published: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Edition: | 1st ed. 2007. |
| Series: | Springer Series in Statistics
|
| Subjects: | |
| Online Access: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
L'impression du document génère 353 p. Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Correlated Data Analysis: Modeling, Analytics, and Applications, Texte imprimé, 9780387565583 • Correlated Data Analysis: Modeling, Analytics, and Applications, Texte imprimé, 9781441924407 • Correlated data analysis, modeling, analytics, and applications, Peter X.-K. Song, New York, Springer, 2007, 1 vol. (XV-346 p.), Springer series in statistics, 978-0-387-71392-2 |
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| 100 | 1 | |a Song, Peter Xue-Kun, |d 1964- | |
| 245 | 1 | 0 | |a Correlated data analysis : |b modeling, analytics, and applications |c Peter X.-K. Song. |
| 250 | |a 1st ed. 2007. | ||
| 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 L'impression du document génère 353 p. | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Bibliogr. Index | ||
| 505 | 1 | |a 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 | |
| 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 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) | ||
| 650 | |a Statistique mathématique | ||
| 650 | |a Corrélation (statistique) | ||
| 650 | |a Equations d'estimation généralisées | ||
| 650 | |a Méthode longitudinale | ||
| 776 | 0 | |t Correlated Data Analysis: Modeling, Analytics, and Applications |b Texte imprimé |z 9780387565583 | |
| 776 | 0 | |t Correlated Data Analysis: Modeling, Analytics, and Applications |b Texte imprimé |z 9781441924407 | |
| 776 | 0 | |0 118372777 |t Correlated data analysis |o modeling, analytics, and applications |f Peter X.-K. Song |c New York |n Springer |d 2007 |p 1 vol. (XV-346 p.) |s Springer series in statistics |z 978-0-387-71392-2 | |
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