Remote sensing data present peculiar features and characteristics that may make their statistical processing and analysis a difficult task. Among them, it can be mentioned the volume of data involved, the redundancy, the ...

Principal component analysis is a technique widely used for studying the movements of proteins using data collected from molecular dynamics simulations. In spite of its extensive use, the technique has a serious drawback: ...

The main purpose of this article is: given a score matrix called S, find out the joint proportional contribution of factors due to persons (conditions, situations, and so forth) and factors due to variables, for any sij ...

A new approach for testing batch "similarity" through comparison of drug dissolution profiles, based on principal component analysis with the establishment of a confidence region (PCA-CR), is presented. The dissolution ...

Gdpc is an R package for the computation of the generalized dynamic principal components proposed in Peña and Yohai (2016). In this paper, we briefly introduce the problem of dynamical principal components, propose a ...

All known approaches to nonlinear principal components are based on minimizing a quadratic loss, which makes them sensitive to data contamination. A predictive approach in which a spline curve is fit minimizing a residual ...

To understand how the tropopause annual evolution relates to the troposphere and lower stratosphere over southern South America, the study analyzes the joint behavior of single and double thermal tropopauses with the 500 ...

El presente estudio trata sobre diferentes criterios para la retención de componentes en el análisis de componentes principales (PCA) aplicado a escalas tipo Likert, que son comunes en los cuestionarios psicológicos. El ...

We herein introduce a general procedure to capture the relevant information from a functional data set in relation to a statistical method used to analyze the data, such as, classification, regression or principal components. ...