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Antedependence Models for Longitudinal Data
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Antedependence Models for Longitudinal Data

by Dale L. Zimmerman , Dale L. Zimmerman , Spain) Nunez-Anton Bilbao Vicente A. , Vicente A. Nunez-Anton

Book Details

Format Paperback / Softback
Book Series Chapman & Hall/CRC Monographs on Statistics and Applied Probability
ISBN-10 113811362X
ISBN-13 9781138113626
Publisher Taylor & Francis Ltd
Imprint CRC Press
Country of Manufacture GB
Country of Publication GB
Publication Date Jun 14th, 2017
Print length 288 Pages
Weight 530 grams
Product Classification: Probability & statistics Biology, life sciences
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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.

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.

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