Student Seminar Series - May 19, 2005
University of Minnesota
School of Statistics
College of Liberal Arts
Statistical
Modeling of Multivariate Longitudinal Binary Data with a Latent
Variable
Rong Yang
Thursday, May 19, 2005
3:00 PM, 127
Ford Hall
Minneapolis, East Bank Campus
Refreshments at 2:30 PM
300 Ford Hall
Abstract
Multivariate longitudinal binary (MLB) data are commonly seen in
applications. This thesis research focuses on developing new
statistical modeling procedures for analyzing MLB data with a latent
variable. A two-stage model is proposed for describing the time course
of a latent variable which is believed to drive all the observable
response variables, and for describing the relationship between the
underlying variable and response variables. Based on the generalized
estimating equations method, a blocked iterative algorithm is proposed
for parameter estimation. This model is generalized in three different
ways to accommodate both time-invariant and time-variant covariates.
This research is motivated by a tantrum data set, which has eight
longitudinal binary responses with a latent variable, denoting the
anger intensity, and several covariates.