Re-sampling methods for estimating the distribution of descriptive statistics of functional data are considered. Through Monte-Carlo simulations, we compare the performance of several re-sampling methods commonly used for estimating the distribution of descriptive statistics. We introduce two re-sampling methods that rely on functional principal component analysis, where the scores were randoml...

In real world problems of nonlinear model building there may be a number of inputs available for use. However, a common problem is that we do not know which inputs are necessary for the model. Previous methods have difficulties in coping with dependent inputs. In this paper, we propose a novel method of input variable selection based on independent component analysis and higher order cross stat...

The problem of input variable selection is well known in the task of modeling real-world data. In this paper, we propose a novel model-free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches w...

In this paper, we introduce some methods for finding mutually corresponding dependent components from two different but related data sets in an unsupervised (blind) manner. The basic idea is to generalize cross-correlation analysis by taking into account higher-order statistics. We propose independent component analysis (ICA) type extensions for the singular value decomposition of the crosscorr...

An extension of the FastICA algorithm is proposed for the blind separation of both Q-proper and Q-improper quaternionvalued signals. This is achieved by maximising a negentropy-based cost function, and is implemented using the Newton method in the augmented quaternion statistics framework. It is shown that the use of augmented statistics and the associated widely linear modeling provide theoret...

4 Item and scale analysis 9 4.1 Dimension reduction through factor analysis and cluster analysis . . . . . . 10 4.1.1 Minimum Residual Factor Analysis . . . . . . . . . . . . . . . . . . . 12 4.1.2 Principal Axis Factor Analysis . . . . . . . . . . . . . . . . . . . . . 13 4.1.3 Alpha Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.1.4 Weighted Least Squares Factor Anal...

3 Item and scale analysis 5 3.1 Dimension reduction through factor analysis and cluster analysis . . . . . . 5 3.1.1 Minimum Residual Factor Analysis . . . . . . . . . . . . . . . . . . . 7 3.1.2 Principal Axis Factor Analysis . . . . . . . . . . . . . . . . . . . . . 8 3.1.3 Weighted Least Squares Factor Analysis . . . . . . . . . . . . . . . . 8 3.1.4 Principal Components analysis (PCA) . . ....