. | title | author |
---|---|---|
*+DHLZ: | Analysis of Longitudinal Data | Diggle, Heagerty, Liang, and Zeger |
*+VM : | Linear Mixed Models for Longitudinal Data | Verbeke and Molenberghs |
+MN: | Generalized Linear Models | McCullagh and Nelder |
+PB: | Mixed-Effects Models in S and S-PLUS | Pinheiro and Bates |
FT: | Multivariate Statistical Modelling Based on Generalized Linear Models | Fahrmeir and Tutz |
SCM: | Variance Components | Searle, Casella and McCulloch |
+CH: | Analysis of Repeated Measures | Crowder and Hand |
Li: | Models for Repeated Measurements | Lindsey |
He: | Quasilikelihood and its Application | Heyde |
Lo: | Random Coefficient Models | Longford |
Go: | Multilevel Statistical Models | Goldstein |
MS: | Generalized, Linear, and Mixed Models | McCulloch and Searle |
This course will extend linear model methods to methods for analysis of data with non-iid errors, including generalized linear models for data with nonconstant variance, quasilikelihood for overdispersed data, and several methods for correlated data such as random effects and mixed models, analysis of repeated measures, longitudinal data analysis, and generalized estimating equations. The primary objective of the course is to provide some tools for analysis of data with non-iid errors, and highlight the common features of these tools to give a general approach to the analysis of such data.