|
|
 |
Search published articles |
 |
|
Showing 2 results for Nadifar
Afshin Fallah, Mahsa Nadifar, Ramin Kazemi, Volume 7, Issue 1 (9-2013)
Abstract
In this paper the regression analysis with finite mixture bivariate poisson response variable is investigated from the Bayesian point of view. It is shown that the posterior distribution can not be written in a closed form due to the complexity of the likelihood function of bivariate Poisson distribution. Hence, the full conditional posterior distributions of the parameters are computed and the Gibbs algorithm is used to sampling from posterior distributions. A simulation study is performed in order to assess the proposed Bayesian model and its efficiency in estimation of the parameters is compared with their frequentist counterparts. Also, a real example presented to illustrate and assess the proposed Bayesian model. The results indicate to the more efficiency of the estimators resulted from Bayesian approach than estimators of frequentist approach at least for small sample sizes.
Mahsa Nadifar, Hossein Baghishani, Afshin Fallah, Volume 15, Issue 1 (9-2021)
Abstract
Many of spatial-temporal data, particularly in medicine and disease mapping, are counts. Typically, these types of count data have extra variability that distrusts the classical Poisson model's performance. Therefore, incorporating this variability into the modeling process, plays an essential role in improving the efficiency of spatial-temporal data analysis. For this purpose, in this paper, a new Bayesian spatial-temporal model, called gamma count, with enough flexibility in modeling dispersion is introduced. For implementing statistical inference in the proposed model, the integrated nested Laplace approximation method is applied. A simulation study was performed to evaluate the performance of the proposed model compared to the traditional models. In addition, the application of the model has been demonstrated in analyzing leukemia data in Khorasan Razavi province, Iran.
|
|