A bivariate Markov process comprises a pair of random processes which are jointly Markov. One of the two processes in that pair is observable while the other plays the role of an underlying process. We are interested in three classes of bivariate Markov processes. In the first and major class of interest, the underlying and observable processes are continuous-time with finite alphabet; in the s...

Background and Objectives: Cancer is a complex disease with a lengthy and expensive course of treatment that causes many problems for the community. Knowledge of oral cancer plays an important role in early diagnosis. The aim of this study was to determine the level of knowledge about the symptoms and risk factors of oral cancer and assess the related factors.   Methods: In this study, 671 pa...

This paper represents the comparison between Negative Binomial Regression model and Generalized Poisson Regression model for over-dispersion count data. For this comparison, we used BDHS 2007 data in where the response variable is the total children ever born which is a count data. When the response variable is count, then Poisson Regression Model as a Generalized Linear Model is widely and pop...

The bivariate Poisson model is the most widely used for counts, and in recent years, several regression models have been developed order to analyse two response variables that are possibly correlated. In this paper, a particular class of model, from Bernoulli will be presented investigated. proposed use dependence parameters can positively negatively correlated data, whereas more well-known mod...

Excess zeros and overdispersion are commonly encountered phenomena that limit the use of traditional Poisson regression models for modeling count data. The focus of this paper is on modeling count data in the case that a population has excess zero counts and also consists of several sub-populations in the non-zero counts. The proposed zero-inflated Poisson regression mixture model accounts for ...