Dimension reduction methods play an important role in multivariate statistical analysis, in particular with high-dimensional data. Linear methods can be seen as a linear mapping from the original feature space to a dimension reduction subspace. The aim is to transform the data so that the...

A growing interest in clustering spatial data is emerging in several areas, from local economic development to epidemiology, from remote sensing data to environment analyses. However, methods and procedures to face such problem are still lacking. Local measures of spatial autocorrelation aim at...

In this paper we describe the Xlisp-Stat version of the sm library, a software for applying nonparametric kernel smoothing methods. The original version of the sm library was written by Bowman and Azzalini in S-Plus, and it is documented in their book Applied Smoothing Techniques for Data...