Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client’s degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform database. We use classical methods such as discriminan analysis (DA), logit and probit models as benchmark O...

Probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. The problem of path overlapping in network modelling has been one of the main issues to be tackled. Due to its flexible covariance structure, probit model can adequately address the pro...

Binary response regression is a useful technique for analyzing categorical data. Popular binary models use special link functions such as the logit or the probit link. We assume that the inverse link function H is a random member of the class of normal scale mixture cdfs. We propose three di erent models for this random H : (i) H is a nite scale mixture with a Dirichlet distribution prior on th...

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Two parameters, C and r, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GA-SVM) model that can automatically determine the optimal parameters, C and r, of SVM with the highest predictive accuracy and generalization ability simultaneously. This paper pioneered on employing a ...

A probit model is used to examine the stability of the predictive content of the term structure in forecasting U.S. recessions. In particular, we compare forecasts of a recession under different assumptions regarding the presence of a structural break. We find strong evidence of the existence of a structural break in the U.S. economy, but there is considerable uncertainty about its exact locati...

This article provides a framework for estimating the marginal likelihood for the purpose of Bayesian model comparisons . The approach extends and completes the method presented in Chib (1995) by overcoming the problems associated with the presence of intractable full conditional densities. The proposed method is developed in the context of MCMC chains produced by the Metropolis–Hastings algorit...