In parameter estimation, it is not a good choice to select a “best model” by some criterion when there is model uncertainty. Model averaging is commonly used under this circumstance. In this paper, transformation-based model averaged tail area is proposed to construct confidence interval,...

This paper develops a Bayesian spike and slab model for zero-inflated count models which are commonly used in health economics. We account for model uncertainty and allow for model averaging in situations with many potential regressors. The proposed techniques are applied to a German data set...

<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...

In this paper we propose a new nonparametric regression method called composite support vector quantile regression (CSVQR) that combines the formulations of support vector regression and composite quantile regression. First the CSVQR using the quadratic programming (QP) is proposed and then the...

In recent years in the fields of statistics and machine learning an increasing amount of so called local classification methods has been developed. Local approaches to classification are not new, but have lately become popular. Well-known examples are the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$k$$</EquationSource> </InlineEquation> nearest neighbors method and...</equationsource></inlineequation>