Shults J., Hilbe J.M. Quasi-Least Squares Regression

Boca Raton: CRC Press Taylor & Francis Group, 2014. — 200 p. — e-ISBN: 978-1-4200-9994-2.
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability (Book 132).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.Review of Generalized Linear Models and Generalized Estimating Equations.
Quasi-Least Squares Theory and Applications.
History and Theory of Quasi-Least Squares Regression.
Mixed Linear Structures and Familial Data.
Correlation Structures for Clustered and Longitudinal Data.
Analysis of Data with Multiple Sources of Correlation.
Correlated Binary Data.
Assessing Goodness of Fit and Choice of Correlation Structure for QLS and GE.
Sample Size and Demonstration.