TITLE: Dynamic Generalized Linear Models for Correlated Binary Responses

ABSTRACT:

Dynamic generalized linear model is developed to model time series of correlated binary response data. Scale mixture of multivariate normal links are used to develop a general class of link functions to model such data. This large class of link functions include multivariate probit, Student's t, exponential power family and logit links as a special case. Markov chain Monte Carlo method is used to simulate from posterior distributions. Bayesian inference, model diagnostics and model selections are considered. The proposed methodology is illustrated on a real data set on house price from Dade county , Florida over a period of 22 years, where the objective is to model binary responses of house prices (high versus low) in presence of assessed value, age and square feet.
 

COFFEE: 3:45 p.m., 104 Snedecor Hall