The objective of this project is to fit a sequence of increasingly complex zero-inflated censored regression models to a known data set. It is quite common to find censored count data in statistical analyses of health-related data. Modeling such data while ignoring the censoring, zero-inflation, and overdispersion often results in biased parameter estimates. This project develops various regression models that can be used to predict a count response variable that is affected by various predictor variables. The regression parameters are estimated with Bayesian analysis using a Markov chain Monte Carlo (MCMC) algorithm. The tests for model adequacy are discussed and the models are applied to an observed data set.
College and Department
Physical and Mathematical Sciences; Statistics
BYU ScholarsArchive Citation
Prasad, Jonathan P., "Zero-Inflated Censored Regression Models: An Application with Episode of Care Data" (2009). Theses and Dissertations. 2226.
zero-inflation, over-dispersion, censoring, Poisson, generalized Poisson, negative binomial, Bayesian MCMC, Proc MCMC, health care