, with the use of an alternative to the standard principal components approach – independent components analysis (ICA). ICA … seeks higher moment independence and maximises in relation to a chosen risk parameter. We apply an ICA based on kurtosis … maximisation to weekly US REIT data using a kurtosis maximising algorithm. The results show that ICA is successful in capturing the …

, called independent component analysis (ICA), makes uses of statistical principles. The main disadvantage is the fact that the …

reduction using independent component analysis (ICA) and classified using SVM. The obtained results of SVM and SVM with ICA are …

In this paper, we apply independent component analysis (ICA) for prediction and signal extraction in multivariate time … series data. We compare the performance of three different ICA procedures, JADE, SOBI, and FOTBI that estimate the components … performance of those three ICA procedures on capturing the dynamic relationships among the industrial production index (IPI) time …

Component Analysis (PCA), Independent Component Analysis (ICA) and Sparse Random Projections (SRP). The performance of rotation …, ICA3 based Rotation Forests outperform all other considered classifiers and are therefore recommended as a well …

analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying …, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non … simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally …