ABSTRACT The multivariate exponential power is a useful distribution for modeling departures from normality in data by means of tail weight scalar parameter that regulates the non-normality model. incorporation shape asymmetry vector into model serves to account potential asymmetries and gives rise skew distribution. This work aimed at revisiting taking as starting point its formulation scale m...

We propose a practical and flexible method to introduce skewness in multivariate symmetric distributions. Applying this procedure to the multivariate Student density leads to a “multivariate skew-Student” density, in which each marginal has a specific asymmetry coefficient. Combined with a multivariate GARCH model, this new family of distributions is found to be more useful than its

This paper provides a practical simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods and maximum likelihood estimates are obtained by a Monte Carlo version of the EM algorithm. A practical approach for the computation of Bayes factors from the simulation outp...

The logistic normal distribution has recently been adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many ...

Skewness is often present in a wide range of applied problems. One possible approach to model this skewness is based on the class of skew normal distributions. Fitting such distributions remains an inference challenge in various cases. In this paper, we propose and study novel estimators using weighted moments for the closed multivariate skew-normal distribution.

aim : the aim of this study was to determine whether there is relation between body mass index and symptoms of gastro-esophageal reflux disease in our community using logit, probit and complementary log-log models. background : the most frequent statistical tool to address the relationship among a dichotomous response and other covariates is logistic regression. however logistic regression is f...

The market penetration of shared micro-mobility services (shared bikes, e-bikes, and e-scooters) suggest that young highly educated people constitute a group is more eager to use such services. However, little known about the factors influencing their behavioral intention as system. analysis may provide insights on adoption by modeling them system using multivariate ordered probit. This paper c...

We examine the impact of several chronic diseases on the probability of labour force participation using data from the Australian National Health Surveys. An endogenous multivariate probit model is used to account for the potential endogeneity of the incidence of chronic conditions such as diabetes, cardiovascular diseases and mental illnesses. The cross-equation correlations are significant, r...

A rich class of parametric models is proposed for discrete choice data based on the scale mixture of multivariate normal distributions. The multinomial probit model is a special case in the class. The new models can be implemented in a Bayesian framework without much difficulty because of their special connections to the multinomial probit model. A Gibbs sampler with data augmentation is used t...