Generalized Estimating Equation and Generalized Linear Mixed Models
This chapter covers methods to model observations that are correlated. The modeling of correlated data requires an alternative to the joint likelihood to obtain the parameter estimates. One such method is based on modeling the marginal mean and another me
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Jeffrey R. Wilson Elsa Vazquez-Arreola (Din) Ding-Geng Chen
Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates
Emerging Topics in Statistics and Biostatistics Series Editor (Din) Ding-Geng Chen, University of North Carolina, Chapel Hill, NC, USA Editorial Board Members Andriëtte Bekker, University of Pretoria, Pretoria, South Africa Carlos A. Coelho, Universidade Nova de Lisboa, Caparica, Portugal Maxim Finkelstein, University of the Free State, Bloemfontein, South Africa Jeffrey R. Wilson, Arizona State University, Tempe, AZ, USA
More information about this series at http://www.springer.com/series/16213
Jeffrey R. Wilson • Elsa Vazquez-Arreola (Din) Ding-Geng Chen
Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates
Jeffrey R. Wilson Department of Economics W. P. Carey School of Business Arizona State University Chandler, AZ, USA
Elsa Vazquez-Arreola School of Mathematical and Statistical Sciences Arizona State University Tempe, AZ, USA
(Din) Ding-Geng Chen School of Social Work & Department of Biostatistics University of North Carolina Chapel Hill, NC, USA Department of Statistics University of Pretoria Pretoria, South Africa
ISSN 2524-7735 ISSN 2524-7743 (electronic) Emerging Topics in Statistics and Biostatistics ISBN 978-3-030-48903-8 ISBN 978-3-030-48904-5 (eBook) https://doi.org/10.1007/978-3-030-48904-5 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
I dedicate this to my students, present and past. Their insight had a great deal to do with the materials covered in this book. Jeffrey R. Wilson I dedicate this to my family for their unconditional support. Elsa
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