Zero-Inflated Poisson Model of Claims Frequencies Using a Maximum Likelihood and a Bayesian Approach
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics
Date of Award
Summer 2022
Abstract
A zero-inflated Poisson model is applied to analyze claims data sets found in literature from various sources. Maximum likelihood estimation and Bayesian approaches are used to analyze the data and the results of each method are compared. A Metropolis-Hastings Markov chain Monte Carlo algorithm is used to simulate the posterior distribution of the model parameters in the Bayesian approach. Original scripts are written in R to implement the methods. Results from the maximum likelihood and Bayesian implementations are compared.
Advisor
Thomas Boucher
Subject Categories
Mathematics | Physical Sciences and Mathematics
Recommended Citation
Dixon, Ricky, "Zero-Inflated Poisson Model of Claims Frequencies Using a Maximum Likelihood and a Bayesian Approach" (2022). Electronic Theses & Dissertations. 1006.
https://digitalcommons.tamuc.edu/etd/1006
