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Statistics Colloquium/PhD Defense
November 6 (Fri) 2 p.m.
3307 Rood Hall
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 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 proposed model characterizes spatial and cross-spatial dependencies. Inferences on model parameters and predictions are done using samples from a Markov Chain Monte Carlo algorithm. In addition, a method for variable selection through Bayesian penalized regression is developed using a LASSO-type method, elastic net.
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All statistics students are expected to attend.