drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. identification of resistant phenotype is very important because it can lead to effective treatment plan. there is an interest in developing classifying models of resistance phenotype based on the multivariate data. we have investigated a vibrational spectroscopic approach in order to characterize a sen...

Statistically independent features can be extracted by nding a factorial representation of a signal distribution. Principal Component Analysis (PCA) accomplishes this for linear correlated and Gaussian distributed signals. Independent Component Analysis (ICA), formalized by Comon (1994), extracts features in the case of linear statistical dependent but not necessarily Gaussian distributed signa...

Abstrait Statistical modeling and analysis is extensively used in businesses for various purposes including graphic visualization of data, measurement of central tendencies and other statistics, and inferences on populations based on samples. Data are the fundamental component of each of these activities. In this paper an extension of the standard database language SQL for statistical modeling ...

climate is a multivariate system. every change in every climate variable could result in different changes in climate. recently, experts have paid extensive attention to frequency distribution sequence of climate elements. accordingly, many different indices have been defined. precipitation extremes can exhibit the phase space of climate system. a technique that can be used to analyze climate p...

The principal component analysis (also named Karhunen–Loève transformation) and the factor analysis are both tools of the multivariate statistics, more precisely the exploratory data analysis. They are used e.g. in data mining or machine learning. Although they share the same goal, they reach it with different methods. Over the years, some misunderstandings came up, how these methods differ fro...