Abstract #300296

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JSM 2003 Abstract #300296
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 - #300296
Title: Zero-Inflated Models for Correlated Data
Author(s): Sujit Kumar Ghosh*+
Companies: North Carolina State University
Address: Dept. of Statistics, Raleigh, NC, 27695-8203,
Keywords: Bayesian inference ; MCMC ; mixed models ; zero-inflated distributions
Abstract:

Statistical methods for analyzing count data with excess zeros are very important in various scientific fields. In modeling failure counts collected from more matured manufacturing processes, there are usually a relatively large number of zeros. The work formulates several kinds of models for statistically correlated observations on a count response that allow for both of these possibilities. The goals are to: (a) develop new flexible regression models for zero-inflated correlated data that explicitly relate the count response to the available explanatory variables through a link function, (b) develop efficient algorithms based on Markov chain Monte Carlo methods to fit the proposed models, (c) test the impact of explanatory variables on the zero-counts, and (d) provide predictions, and appropriate measures of prediction uncertainties, at future times by accounting for the uncertainty.

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