Parameter Estimation and Hypothesis Testing of Compound Correlated Bivariate Poisson Regression Models with Exposure Variable Syarifah Nisrina Hasna Salby (a*), Purhadi (a), Bambang Widjanarko Otok (a)
a) Department of Statistics, Institut Teknologi Sepuluh Nopember
*sy.nisrinahasna[at]gmail.com
Abstract
Compound Correlated Bivariate Poisson Regression (CCBPR) is a regression model that combines the Poisson distribution with other distributions. The CCBPR model is proposed to overcome correlated count data with overdispersion issue. In this study, CCBPR is approached by Generalized Gaussian Inverse (GIG) distribution. The aim of this study is to obtain parameter estimation and hypothesis testing by adding the exposure variable as a development of CCBPR model. The estimation procedure is conducted by Maximum Likelihood Estimation (MLE), while Maximum Likelihood Ratio Test (MLRT) is used for hypothesis testing.
