Multivariate random effect models with complete and incomplete data

Multivariate random effect models with complete and incomplete data

This paper considers the problem of estimating fixed effects, random effects and variance components for the multi-variate random effects model with complete and incomplete data. It also considers making inference about the fixed and random effects, a problem which requires careful consideration of the choice of degrees of freedom to use in confidence intervals. This paper uses the EM algorithm...

Complete and Incomplete Market Models

Linear Models Analysis of Incomplete Multivariate Categorical Data

• • • LIST OF TABLES. LIST OF FIGURES i vi viii PART I: CHAPTER ESTIMATION, TESTING AND MODELING PROCEDURES

Marginalized transition random effects models for multivariate longitudinal binary data

Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, interpretation and computation of marginal covariate effects can be difficult. Heagerty has proposed marginally specified logistic-normal models (1999) and marginalized transition models (2002) for longitudinal binary and categorical data in which the marginal mean is ...

Parameterization of multivariate random effects models for categorical data.

Alternative parameterizations and problems of identification and estimation of multivariate random effects models for categorical responses are investigated. The issues are illustrated in the context of the multivariate binomial logit-normal (BLN) model introduced by Coull and Agresti (2000, Biometrics 56, 73-80). We demonstrate that the BLN model is poorly identified unless proper restrictions...