In this paper a new approach, referred to as expected-mode augmentation (EMA), for multiple-model (MM) estimation is proposed. It is intended to enhance the performance of an MM algorithm in the cases of a continuous mode space. In this approach, the original fixed or variable model set is augmented by a model that intends to match the expected value of the true mode, which is readily computed ...

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require t...

We discuss practical methods for the assessment, comparison and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate in order to describe the associat...