Abstract #300677

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JSM 2003 Abstract #300677
Activity Number: 293
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300677
Title: Dynamic Generalized Linear Model for Correlated Binary Responses
Author(s): Amitabha Bhaumik*+ and Dipak K. Dey
Companies: University of Connecticut
Address: 1 Northwood Rd., Storrs Mansfield, CT, 06268-1900,
Keywords: Bayesian computation ; CPO ; deviance inference criterion ; dynamic generalized linear model ; scale mixture of multivariate normal links ; weighted L-measure
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 dataset on house price from Dade County, Florida, over a period of 21 years, where the objective is to model binary responses of house prices (high versus low) in presence of assessed value, age, and square feet.

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