Antedependence Models for Longitudinal Data - Couverture souple

Zimmerman, Dale L.; Nez-antn, Vicente A.

Couverture souple

ISBN 10 : 113811362X ISBN 13 : 9781138113626

Editeur : Chapman and Hall/CRC, 2017

Synopsis

The First Book Dedicated to This Class of Longitudinal Models

Although antedependence models are particularly useful for modeling longitudinal data that exhibit serial correlation, few books adequately cover these models. By gathering results scattered throughout the literature, Antedependence Models for Longitudinal Data offers a convenient, systematic way to learn about antedependence models. Illustrated with numerous examples, the book also covers some important statistical inference procedures associated with these models.

After describing unstructured and structured antedependence models and their properties, the authors discuss informal model identification via simple summary statistics and graphical methods. They then present formal likelihood-based procedures for normal antedependence models, including maximum likelihood and residual maximum likelihood estimation of parameters as well as likelihood ratio tests and penalized likelihood model selection criteria for the model’s covariance structure and mean structure. The authors also compare the performance of antedependence models to other models commonly used for longitudinal data.

With this book, readers no longer have to search across widely scattered journal articles on the subject. The book provides a thorough treatment of the properties and statistical inference procedures of various antedependence models.

A propos du livre

À propos de l'auteur

Dale L. Zimmerman is a professor in the Department of Statistics and Actuarial Science at the University of Iowa.

Vicente A. Núnez-Antón is a professor in the Department of Econometrics and Statistics at The University of the Basque Country.

À propos de la quatrième de couverture

By gathering many results scattered throughout the literature, this book offers a convenient, systematic way to learn about antedependence models and the important statistical inference procedures associated with these models. It presents both informal methods of inference, such as graphical methods, and formal likelihood-based methods. The authors also explore related topics and extensions, including alternative estimation methods and multivariate antedependence models. They explain how to use the methodology through illustrative examples and data sets. The relevant R functions are available for download online.

Détails de l'édition

• Éditeur: Chapman and Hall/CRC
• Date d'édition: 2017
• Langue: anglais
• ISBN 10: 113811362X
• ISBN 13: 9781138113626
• Reliure: Broché
• Nombre de pages: 270
• Coordonnées du fabricant: non disponible
• Personne responsable: GPSR Kontakt; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Allemagne