<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>

<Para ID="Par1">A method for robust estimation of dynamic mixtures of multivariate distributions is proposed. The EM algorithm is modified by replacing the classical M-step with high breakdown S-estimation of location and scatter, performed by using the bisquare multivariate S-estimator. Estimates are obtained...</para>

<Para ID="Par1">Contingency tables are often used to display the multivariate frequency distribution of variables of interest. Under the common multinomial assumption, the first step of contingency table analysis is to estimate cell probabilities. It is well known that the unconstrained maximum likelihood...</para>

<Para ID="Par1">In this paper we consider the Bayesian approach to the problem of variable selection in normal linear regression models with related predictors. We adopt a generalized singular <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$g$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>g</mi> </math> </EquationSource> </InlineEquation>-prior distribution for the unknown model parameters and the beta-prime prior for the scaling factor <InlineEquation ID="IEq2"> <EquationSource...</equationsource></inlineequation></equationsource></equationsource></inlineequation></para>

The estimation of probability density functions is one of the fundamental aspects of any statistical inference. Many data analyses are based on an assumed family of parametric models, which are known to be unimodal (e.g., exponential family, etc.). Often a histogram suggests the unimodality of...

The problem of classifying a categorical response Y is considered in a nonparametric framework. The distribution of Y depends on a vector of predictors X, where the coordinates Xj of X may be continuous, discrete, or categorical. An algorithm is constructed to select the variables to be used for...

Standard fixed symmetric kernel-type density estimators are known to encounter problems for positive random variables with a large probability mass close to zero. It is shown that, in such settings, alternatives of asymmetric gamma kernel estimators are superior, but also differ in asymptotic...

An algorithm is proposed for calculating correlation measures based on entropy. The proposed algorithm allows exhaustive exploration of variable subsets on real data. Its time efficiency is demonstrated by comparison against three other variable selection methods based on entropy using 8 data...

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence...

One of the features inherent in nested Archimedean copulas, also called hierarchical Archimedean copulas, is their rooted tree structure. A nonparametric, rank-based method to estimate this structure is presented. The idea is to represent the target structure as a set of trivariate structures,...