Background: Congenital Hypothyroidism (CH) is one of the reasons for mental retardation and defective growth in neonates. It can be treated if it is diagnosed early. The congenital hypothyroidism can be diagnosed using newborn screening in the first days after birth. Disease mapping helps to identify high-risk areas of the disease. This study aimed to evaluate the pattern of CH using the Poisso...

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, we formulate and estimate the spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from a...

Many phenomena are correlated and spatially dependent. In addition, some of them ordinal discrete responses. Thus, a model is needed to capture the interactions multivariate outcomes spatial dependences. Following Smith LeSage (2004) Jeliazkov et al. (2008), this study proposes new algorithm for ordered probit (MSOP) address need. applying model, parameters calculated using Bayesian inference b...

background: birth weight and gestational age are two important variables in obstetric research. the primary measure of gestational age is based on a mother's recall of her last menstrual period. this recall may cause random or systematic errors. therefore, the objective of this study is to utilize bayesian mixture model in order to identify implausible gestational age.   methods: in this cross-...

Small area models have become popular methods for producing reliable estimates sub-populations (small geographic areas in this study). modeling may be carried out via model-assisted approaches within the model-based or design-based paradigm. When there are medium large samples, a approach reliable. However, when data scarce, technique required. Model-based Bayesian analysis is its ability to co...

We introduce the matrix exponential as a way of modelling spatially dependent data. The matrix exponential spatial specification simplifies the loglikelihood allowing a closed form solution to the problem of maximum likelihood estimation, and greatly simplifies Bayesian estimation of the model. The matrix exponential spatial specification can produce estimates and inferences similar to those fr...

Taking a Bayesian perspective on model comparison for crosssectional and static panel data models considerably simplifies the task of selecting an appropriate model. A wide variety of alternative specifications that include various combinations spatial dependence in lagged values of the dependent variable, spatial lags of the explanatory variables, as well as dependence in the model disturbance...