Tree-based methods are statistical procedures for automatic learning from data, whose main applications are integrated into a data-mining environment for decision support systems. Here, we focus on two problems of decision trees: the stability of the rules obtained and their applicability to...

<Para ID="Par1">A discrepancy function provides for an evaluation of a candidate model by quantifying the disparity between the candidate model and the true model that generated the observed data. The favored model from a candidate class is the one judged to have minimum discrepancy with the true model. The...</para>

<Para ID="Par1">We give sufficient conditions which the mutation rate must satisfy for the convergence of the genetic algorithm when that rate is allowed to change throughout iterations. The empirical performance of the algorithm with regards to changes in the mutation parameter is explored via test functions,...</para>

<Para ID="Par1">High dimensional data sets are now frequently encountered in many scientific fields. In order to select a sparse set of predictors that have predictive power and/or provide insightful understanding on which predictors really influence the response, a preliminary variable screening is typically...</para>

A threshold stochastic volatility (SV) model is used for capturing time-varying volatilities and nonlinearity. Two adaptive Markov chain Monte Carlo (MCMC) methods of model selection are designed for the selection of threshold variables for this family of SV models. The first method is the...