Research Article
Bayesian Zero Inflated Negative Binomial Regression Model for The Parkinson Data
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290530, author={Shafira Shafira and Sarini Abdullah and Dian Lestari}, title={Bayesian Zero Inflated Negative Binomial Regression Model for The Parkinson Data}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={count data excess zero markov chain monte carlo over-dispersion parkinson’s disease}, doi={10.4108/eai.2-8-2019.2290530} }- Shafira Shafira
Sarini Abdullah
Dian Lestari
Year: 2020
Bayesian Zero Inflated Negative Binomial Regression Model for The Parkinson Data
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290530
Abstract
Excess zeros can be solved by Zero Inflated Poisson (ZIP). If over-dispersion still exists in the data, the ZIP model is no longer suitable. Replacing the Poisson distribution with negative binomial distribution in the counting process may provide an alternative solution. Zero Inflated Negative Binomial (ZINB) regression model is estimated using the Bayesian method. Conjugate non-informative priors were used. Sampling parameters from posterior distribution is conducted using Markov Chain Monte Carlo (MCMC) simulation with 50,000 burn-in and 150,000 iterations. The model was then implemented to Parkinson’s disease data obtained from the Parkinson’s Progression Markers Initiative (PPMI) program. The MCMC result showed the convergence of the parameters. The result of the inspection of motoric aspect was significant in explaining does Parkinson’s patients have to consume drugs or not. The result of the inspection of non-motoric aspect and body response were significant in explaining motoric complication in Parkinson's disease sufferers.