Description
Springer Correlated Data Analysis Modeling Analytics And Applications by Peter X. -K. Song
This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas._x000D_ _x000D_
and Examples.- Dispersion Models.- Inference Functions.- Modeling Correlated Data.- Marginal Generalized Linear Models.- Vector Generalized Linear Models.- Mixed-Effects Models: Likelihood-Based Inference.- Mixed-Effects Models: Bayesian Inference.- Linear Predictors.- Generalized State Space Models.- Generalized State Space Models for Longitudinal Binomial Data.- Generalized State Space Models for Longitudinal Count Data.- Missing Data in Longitudinal Studies._x000D_