Variance Components : Mixed Models, Methodologies and Applications

Variance Components Estimation deals with the evaluation of the variation between observable data or classes of data.

This is an up-to-date, comprehensive work that is both theoretical and applied.

Topics include ML and REML methods of estimation; Steepest-Acent, Newton-Raphson, scoring, and EM algorithms; MINQUE and MIVQUE, confidence intervals for variance components and their ratios; Bayesian approaches and hierarchical models; mixed models for longitudinal data; repeated measures and multivariate observations; as well as non-linear and generalized linear models with random effects.