Researchers have shown that the optimally weighted z-test, where the weights are the standardized expected difference in means, is more powerful than other methods when combining p-values from independent studies. However, in practice the effect for each independent study is usually unknown,...

A way to achieve feature selection for classification problems polluted by label noise is proposed. The performances of traditional feature selection algorithms often decrease sharply when some samples are wrongly labelled. A method based on a probabilistic label noise model combined with a...

A software package, rBeta2009, developed to generate beta random numbers and Dirichlet random vectors in R is presented. The package incorporates state-of-the-art algorithms so as to minimize the computer generation time. In addition, it is designed in a way that (i) the generation efficiency is...

A class of efficient and economical response surface designs that can be constructed using known designs is introduced. The proposed class of designs is a modification of the Central Composite Designs, in which the axial points of the traditional central composite design are replaced by some...

Robust mixture modeling approaches using skewed distributions have recently been explored to accommodate asymmetric data. Parsimonious skew-t and skew-normal analogues of the GPCM family that employ an eigenvalue decomposition of a scale matrix are introduced. The methods are compared to...

In many applications, it is of interest to simultaneously cluster row and column variables in a data set, identifying local subgroups within a data matrix that share some common characteristic. When a small set of variables is believed to be associated with a set of responses, block clustering...

Finding a best designed experiment based on balancing several competing goodness measures of the design is becoming more important in many applications. The Pareto front approach allows the practitioner to understand trade-offs between alternatives and make more informed decisions. Efficient...

Singular Spectrum Analysis (SSA) is a powerful tool of analysis and forecasting of time series. The main features of the Rssa package, which efficiently implements the SSA algorithms and methodology in R, are described. Analysis, forecasting and parameter estimation are demonstrated using case...

Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction...

Modified MIR is a Monte-Carlo algorithm used for bootstrapping minimum information ratios in order to assess the number of unknown components in finite mixtures. The method was proposed as a modification of the minimum information ratio (MIR) method, and was proved to outperform it. Further...