Date of Award
Open Access Thesis
Datasets with a relatively large number of zeros is commonly seen in medical applications. Although models like Zero-inflated Poisson (ZIP) model are proposed for counts data, there is still some issues with ordinal data which have excess zeros. In this paper, we developed a Bayesian approach to accommodate the excess zero in ordinal data. Intellectual disability (ID), also known as mental retardation (MR), is a disability characterized by below-average intelligence or mental ability and a lack of the learning necessary skills for daily life. A person with intellectual disability has intellectual functioning and adaptive behaviors limitations. Intellectual disability is a life-term disability and usually originates before birth. The ID data set contains numerus zeros since majority of children are normal, and the responses contain scaled levels. Motivated by a frequentist study using EM algorithm to maximize the log-likelihood iteratively for zero-inflated ordinal data, we apply a Bayesian method on the ID data set. The proposed method allows the unknown thresholds of latent variable be flexible and accommodate the excess zero at the same time. A simulation study is also conducted to evaluate the performance of the proposed method with comparison of the regular proportional odds model and frequentist zero-inflated proportional odds model.
Yang, H.(2020). Bayesian Zero-Inflated Model for Ordinal Data. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/5984