Safaa K. Kadhem

Al Muthanna University · Department of Business Administration

This paper focuses on choosing a spatial mixture model with implicitly includes the time to represent the relative risks of COVID-19 pandemic using an appropriate model selection criterion. For this purpose, a more recent criterion so-called the widely Akaike information criterion (WAIC) is used which we believe that its use so limitedly in the con...

Purpose One of the most important sources of energy in the world, due to its great impact on the global economy, is the crude oil. Due to the instability of oil prices which exhibit extreme fluctuations during periods of different times of market uncertainty, it became hard to the governments to predict accurately the prices of crude oil in order t...

This study investigates the spatial heteroge-neity in the maximum monthly rainfall amounts reported by stations in Ireland from January 2018 to December 2020. The heterogeneity is modeled by the Bayesian normal mixture model with different ranks. The selection of the best model or the degree of heterogeneity is implemented using four criteria which...

This paper considers the determination of the order of hidden Markov models. Recently, a proposed predictive measure, the so-called widely applicable information criterion (WAIC), was derived. This criterion is a convenient alternative to the cross-validation approach due to its less computation processes and quick evaluation. We studied the proper...

Recently, there are many works that proposed modeling approaches to describe the random movement of individuals for COVID-19 infection. However, these models have not taken into account some key aspects for disease such the prediction of expected time of patients remaining at certain health state before entering an absorption state (e.g., exit out...

This article aims at identifying the high risk provinces in Iraq using a finite Poisson mixture. Through this methodology, the levels of relative risk is determined through identifying the number of components. In this article we do not investigate spatial correlation among regions and assume that the levels of risk observed in different regions ar...

Recently, there are many of literature that proposed modelling approaches to describe the random movement of individuals for COVID-19 infection. However, those models have not taken into account some key aspects for disease such the prediction of expected time of patients remaining at certain health state before entering an absorption state (e.g. e...

This article aims at identifying the high risk provinces in Iraq using a finite Poisson mixture. Through this methodology, the levels of relative risk is determined through identifying the number of components. The estimation of the model parameters and the model selection are performed using the Bayesian approach which allow to allocate each provi...

In this paper, we focus on choosing a spatial mixture model, with implicitly including the time, to represent the relative risks of COVID-19 pandemic using an appropriate model selection criterion. For this purpose, we used more recent criterion so-called the widely Akaike information criterion (WAIC) which we believe its used in the context of rel...

"This paper aims at the modeling the crashes count in Al Muthanna governance using finite mixture model. We use one of the most common MCMC method which is called the Gibbs sampler to implement the Bayesian inference for estimating the model parameters. We perform a simulation study, based on synthetic data, to check the ability of the sampler to f...

This article presents the databases analyzed and used to evaluate the risk of segment-based roads resulting from traffic crashes for three main motorways in UK from 2010 to 2014. The raw database is collection to many partial data for variables related to compute the crashes rates for each segment. These data were used to develop and select the bes...

About the model selection criteria for hidden markov models

This paper considers the determining the order in hidden Markov models. Recently, a proposed predictive measure so-called widely applicable information criterion (WAIC), was derived as a convenient alternative to cross-validation approach due to the former is more fast computationally alternative. We study properties of this criterion in the Bayesi...

Recently, a paper published by Celeux et al. (2006) introduced several versions for the deviance information criterion (DIC) for mixture models, each version is based on the type of likelihood function. However, no reliable version was adopted for those models. As an idea inspired by Brooks (2002, p. 617), we develop, in this paper, Bayesian devian...

This study considers spatial dependence in the number of injury crashes reported on a road network. The aggregated crash counts are considered realisations of a Poisson random variable; thus, we model both over‐dispersion and serial correlation using the Poisson hidden Markov model (PHMM). PHMMs have typically been used for modelling temporal depen...

Many textbooks aim to cover a range of statistical topics to engage researchers using such methods in a range of disciplines. This book is rather more specialized in its coverage of the modelling of different observed count-process-based time series and would be suitable for statistical researchers and graduate students. It is enhanced with a good...

In Bayesian model selection, the deviance information criterion (DIC) has become a widely used criterion. It is however not defined for the hidden Markov models (HMMs). In particular, the main challenge of applying the DIC for HMMs is that the observed likelihood function of such models is not available in closed form. A closed form for the observe...