Parameter Estimation and Hypothesis Testing of Multivariate Poisson Inverse Gaussian Regression

• Main Authors: Selvi Mardalena, Purhadi Purhadi, Jerry Dwi Trijoyo Purnomo, Dedy Dwi Prastyo
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Symmetry
• Subjects: overdispersion; mixed Poisson; multivariate inverse gaussian regression poisson (MPIGR); exposure; number of mortality
• Online Access: https://www.mdpi.com/2073-8994/12/10/1738

Multivariate Poisson regression is used in order to model two or more count response variables. The Poisson regression has a strict assumption, that is the mean and the variance of response variables are equal (equidispersion). Practically, the variance can be larger than the mean (overdispersion). Thus, a suitable method for modelling these kind of data needs to be developed. One alternative model to overcome the overdispersion issue in the multi-count response variables is the Multivariate Poisson Inverse Gaussian Regression (MPIGR) model, which is extended with an exposure variable. Additionally, a modification of Bessel function that contain factorial functions is proposed in this work to make it computable. The objective of this study is to develop the parameter estimation and hypothesis testing of the MPIGR model. The parameter estimation uses the Maximum Likelihood Estimation (MLE) method, followed by the Newton–Raphson iteration. The hypothesis testing is constructed using the Maximum Likelihood Ratio Test (MLRT) method. The MPIGR model that has been developed is then applied to regress three response variables, i.e., the number of infant mortality, the number of under-five children mortality, and the number of maternal mortality on eight predictors. The unit observation is the cities and municipalities in Java Island, Indonesia. The empirical results show that three response variables that are previously mentioned are significantly affected by all predictors.