Some alternatives for conditional principal component analysis

Ramon Nonell, Santiago Thió‐Henestrosa and Tomàs Aluja‐Banet

Applied Stochastic Models in Business and Industry, 2000, vol. 16, issue 2, 147-158

Abstract: Classically principal component analysis is one of the most used techniques for exploring the multivariate association pattern of variables. On the other hand, conditioning is one of the most promising ideas for controlling the variability of observed data. Here we present a review of some conditioning methods from the analysis of residuals of a parametric model to the analysis of the local variation defined by means of a non‐oriented graph of individuals, this variation being defined from the deviation from a local mean or alternatively from the differences among contiguous vertices. We will compare these approaches and will show that under some conditions they give comparable results. Finally, we will present an example of application to illustrate the results previously stated. Copyright © 2000 John Wiley & Sons, Ltd.

Date: 2000
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/1526-4025(200004/06)16:23.0.CO;2-7

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:16:y:2000:i:2:p:147-158

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().