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
Summary
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
Notes
"A Chapman & Hall book."
Bibliography
Includes bibliographical references (pages 191-200)
