A least squares approach to Principal Component Analysis for interval valued data
AbstractPrincipal Component Analysis (PCA) is a well known technique the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA) recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to analyze how MR-PCA works, the results of a simulation study and two applications on chemical data are proposed.
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Bibliographic InfoPaper provided by University of Molise, Dept. EGSeI in its series Economics & Statistics Discussion Papers with number esdp03013.
Length: 29 pages
Date of creation: 04 Nov 2003
Date of revision:
Principal Component Analysis; Least squares approach; Interval valued data; Chemical data;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-11-09 (All new papers)
- NEP-ECM-2003-11-09 (Econometrics)
- NEP-RMG-2003-11-09 (Risk Management)
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