Quasi-least squares regression / Justine Shults and Joseph M. Hilbe.

Includes bibliographical references (pages 191-200) and index.

Part I: Introduction;
1: Introduction;
2: Review of Generalized Linear Models and Generalized Estimating Equations;

Part II: Quasi-Least Squares Theory and Applications;
3. History and Theory of Quasi-Least Squares Regression;
4. Mixed Linear Structures and Familial Data;
5. Correlation Structures for Clustered and Longitudinal Data;
6. Analysis of Data with Multiple Sources of Correlation;
7. Correlated Binary Data;
8. Assessing Goodness of Fit and Choice of Correlation Structure for QLS and GE;
9. Sample Size and Demonstration;
Bibliography;
Index.

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.