A new way to order independent components

A relatively newer computational technique adopted by statisticians is known as independent component analysis (ICA) which is used to analyze complex multidimensional data with the objective to separate it into components that are independent to each other. Quite often the main interest for conducting ICA is to identify a small number of significant independent components (ICs) to replace the original complex dimensions with. For this, determining the order of identified ICs is a pre-requisite. The area is not unaddressed but it does deserve a careful revisiting. This is the subject matter of the paper which introduces a new method to order ICs. The proposed method is based upon regression approach. It compares the magnitude of the mixing coefficients and regression coefficients of the regression of the original series on ICs. Their compatibility determines the order.     

Three Steps Strategy to Search for Optimum Classification Trees

This article presents a new strategy to construct classification trees. According to the proposed scheme, we focused on keeping the record of sequences of each constructed classification tree; both in terms of splitting predictors and their splitting values in an array. So overall we have as many arrays as we have drawn samples. At this stage, a three steps strategy is introduced, which is used to search for the optimum classification tree. The proposed strategy provides comparable or improved results in terms of generalized error rates than tree and rpart (packages available for classification purposes in the R) using four of the well-known evaluation functions, that is, the Gini, the Entropy, the Twoing, and the Exponent-based function to split nodes for many real-life datasets.     

Pena's statistic for the Liu regression

In fitting regression model, one or more observations may have substantial effects on estimators. These unusual observations are precisely detected by a new diagnostic measure, Pena's statistic. In this article, we introduce a type of Pena's statistic for each point in Liu regression. Using the forecast change property, we simplify the Pena's statistic in a numerical sense. It is found that the simplified Pena's statistic behaves quite well as far as detection of influential observations is concerned. We express Pena's statistic in terms of the Liu leverages and residuals. The normality of this statistic is also discussed and it is demonstrated that it can identify a subset of high Liu leverage outliers. For numerical evaluation, simulated studies are given and a real data set has been analysed for illustration.