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Quasi-Least Squares Regression

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Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages Read more...

Author(s): Justine Shults; Joseph M Hilbe
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Publisher: CRC Press Taylor & Francis Group
Year: 2014

Language: English
Pages: xvii, 200 pages
City: Boca Raton
Tags: Математика;Теория вероятностей и математическая статистика;Математическая статистика;


Content: Part I: Introduction; Chapter 1: Introduction; Chapter 2: Review of Generalized Linear Models and Generalized Estimating Equations; Part II: Quasi-Least Squares Theory and Applications; Chapter 3: History and Theory of Quasi-Least Squares Regression; Chapter 4: Mixed Linear Structures and Familial Data; Chapter 5: Correlation Structures for Clustered and Longitudinal Data; Chapter 6: Analysis of Data with Multiple Sources of Correlation; Chapter 7: Correlated Binary Data; Chapter 8: Assessing Goodness of Fit and Choice of Correlation Structure for QLS and GE; Chapter 9: Sample Size and Demonstration.
Abstract: Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods