Challenges in the analyses of growth mixture models include missing data, outliers, estimation, and model selection. Four non-ignorable missingness models to recover the information due to missing data, and three robust models to reduce the effect of non-normality are proposed. A full Bayesian...

Using Bayesian methods, we reexamine the empirical evidence from Sakoulis et al. (2010) regarding structural breaks in the forward discount for G-7 countries. Our Bayesian framework allows the number and pattern of structural changes in level and variance to be endogenously determined. We find...

This research extends a Pareto/NBD model of customer-base analysis using a hierarchical Bayesian (HB) framework to suit today's customized marketing. The proposed HB model presumes three tried and tested assumptions of Pareto/NBD models: (1) a Poisson purchase process, (2) a memoryless dropout...

In this paper we develop a new Early Warning System (EWS) of Banking Crises based on a new estimated indicator of the gap between the observed private credit ratio and its long-term structural level.

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...

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...

This paper proposes the use of Bayesian approach to implement Value at Risk (VaR) model for both linear and non-linear portfolios. The Bayesian approach provides risk traders with the flexibility of adjusting their VaR models according to their subjective views. First, we deal with the case of...