Antedependence Models for Longitudinal Data

1. Auflage 2017
ISBN: 978-1-138-11362-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 288 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 454 g

Reihe: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

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.

Zielgruppe

Undergraduate

Autoren/Hrsg.

Zimmerman, Dale L.

Núñez-Antón, Vicente A.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Introduction. Unstructured Antedependence Models. Structured Antedependence Models. Informal Model Identification. Likelihood-Based Estimation. Testing Hypotheses on the Covariance Structure. Testing Hypotheses on the Mean Structure. Case Studies. Further Topics and Extensions. Appendices. References. Index.

Über Autor(innen)

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.