We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact the Dynamic Conditional Correlation (DCC) model is extended by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to...

The estimation of multivariate GARCH time series models is a difficult task mainly due to the excessive parametrization exhibited by the problem, usually referred to as the “curse of dimensionality”. For the VEC family, the number of parameters involved in the model grows as a polynomial of...

Markov chain Monte Carlo (MCMC) methods are powerful computational tools for analysis of complex statistical problems. However, their computational efficiency is highly dependent on the chosen proposal distribution, which is generally difficult to find. One way to solve this problem is to use...

Accurate information on patterns of introduction and spread of non-native species is essential for making predictions and management decisions. In many cases, estimating unknown rates of introduction and spread from observed data requires evaluating intractable variable-dimensional integrals. In...

Based on a semiparametric Bayesian framework, a joint-quantile regression method is developed for analyzing clustered data, where random effects are included to accommodate the intra-cluster dependence. Instead of posing any parametric distributional assumptions on the random errors, the...

The Reversible Jump algorithm is one of the most widely used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. A generalized multiple-try version of this algorithm is proposed. The algorithm is based on drawing several proposals at each step and randomly choosing...

In the RC association model for a two-way contingency table, it is often natural to impose order constraints on the score parameters of the row and column variables. In this article, a simple and efficient Bayesian model selection procedure is proposed that simultaneously compares all possible...

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson and Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter...

The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. The dependent variable in the stochastic volatility model is the logarithm of the squared return, and its error distribution is approximated by a mixture of...