Forecasting financial risk and risk measurement methods have been of increasing interest for financial market regulators and financial institutions in the past two decades. While the parametric and semi-parametric models have been widely reviewed in the academic literature, the non-parametric...

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts?variation over and above that accounted for by the Poisson density. The extra-variation ? or dispersion ? is...

Many economics and finance time series are non-Gaussian. In this paper, we propose a Bayesian approach to non-Gaussian autoregressive time series models via quantile functions. This approach is parametric, so we also compare the proposed parametric approach with a semi-parametric approach....

In the present study, an inverse problem to estimate parameters in the Zener model of viscoelasticity based on the generalized fractional element (GFE) network is studied. The Bayesian method is proposed to obtain the optimal estimation of the viscoelastic parameters. Three examples are...

We provide a contingent valuation of a major cycling event, namely the Tour of Flanders. Starting from survey data, we estimate several models with different assumptions about (1) the latent willingness to pay (WTP) distribution and (2) the change in WTP when responding to the follow-up question...

We present a statistical model for voter choice that incorporates a consideration set stage and final vote intention stage. The first stage involves a multivariate probit model for the vector of probabilities that a candidate or a party gets considered. The second stage of the model is a...

The most direct method of design flood estimation is at-site flood frequency analysis, which relies on a relatively long period of recorded streamflow data at a given site. Selection of an appropriate probability distribution and associated parameter estimation procedure is of prime importance...

This article devotes to studying the variance change-points problem in student t linear regression models. By exploiting the equivalence of the student t distribution and an appropriate scale mixture of normal distributions, a Bayesian approach combined with Gibbs sampling is developed to detect...

The EM algorithm is a powerful technique for determining the maximum likelihood estimates (MLEs) in the presence of binary data since the maximum likelihood estimators of the parameters cannot be expressed in a closed-form. In this paper, we consider one-shot devices that can be used only once...

In this paper, the likelihood function and the posterior density function for the parameters given a symmetric trimmed sample are derived. It is assumed that the sample follows the half-normal model. By making use of the Bayesian framework, the predictive density for a single future response is...