Dissertation Proposal
February 21 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower
Bivariate Zero Inflated Negative Binomial Regression for Count Data on a Lattice having Spatial Random Effects
Robert McNutt
Department of Statistics
Western Michigan University
Count data with excess zeros widely occur in ecology, epidemiology, and marketing.
Mixture distributions consisting of a point mass at zero and a separate
discrete distribution are often employed in regression models to account
for excessive zero observations in the data. While Poisson models are very
popular for count data, negative binomial models provide greater
flexibility due to its ability to account for over dispersion.
This research focuses on developing a method for analyzing bivariate count data
with excess zeros collected over a lattice. A bivariate zero-inflated negative
binomial regression model with spatial random effects is developed.
The model characterizes spatial and cross-spatial dependencies. Inference
and predictions are done using a Markov Chain Monte Carlo algorithm.
A method for variable selection through Bayesian penalized regression is
developed using LASSO-type techniques.
All statistics students are expected to attend.
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Past colloquiums