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Quasi-Least Squares Regression

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Quasi-Least Squares Regression

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Description

Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, this book presents a comprehensive treatment of quasi-least squares (QLS) regression--a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEE). The authors present an overview and detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. A fully worked out example is provided that leads readers from the planning stages of a study, including sample size considerations, through model construction and interpretation. Special focus is given to goodness-of-fit analysis and strategies on selecting the appropriate working correlation structure. The text includes additional examples throughout to demonstrate each topic of discussion and uses Stata for the majority of examples, along with corresponding R, SAS, and MATLAB(R) code.

Author Biography:

Justine Shults is an associate professor and co-director of the Pediatrics Section in the Division of Biostatistics in the Perelman School of Medicine at the University of Pennsylvania, where she is the principal investigator of the biostatistics training grant in renal and urologic diseases. She is the Statistical Editor of the Journal of the Pediatric Infectious Disease Society and the Statistical Section Editor of Springer Plus. Professor Shults (with N. Rao Chaganty) developed Quasi-Least Squares (QLS) and was funded by the National Science Foundation and the National Institutes of Health to extend QLS and develop user-friendly software for implementing her new methodology. She has authored or co-authored over 100 peer-reviewed publications, including the initial papers on QLS for unbalanced and unequally spaced longitudinal data and on MARK1ML and the choice of working correlation structure for GEE. Joseph M. Hilbe is a Solar System Ambassador with the Jet Propulsion Laboratory, an adjunct professor of statistics at Arizona State University, and an Emeritus Professor at the University of Hawaii. An elected fellow of the American Statistical Association and an elected member of the International Statistical Institute (ISI), Professor Hilbe is president of the International Astrostatistics Association as well as chair of the ISI Sports Statistics and Astrostatistics committees. He has authored two editions of the bestseller Negative Binomial Regression, Logistic Regression Models, and Astrostatistical Challenges for the New Astronomy. He also co-authored Methods of Statistical Model Estimation (with A. Robinson), Generalized Estimating Equations, Second Edition (with J. Hardin), and R for Stata Users (with R. Muenchen), as well as 17 encyclopedia articles and book chapters in the past five years.
Release date Australia
January 28th, 2014
Audience
  • Tertiary Education (US: College)
Illustrations
17 Tables, black and white; 8 Illustrations, black and white
Pages
221
Dimensions
156x235x18
ISBN-13
9781420099935
Product ID
5447622

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