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November 2018 Searching for the core variables in principal components analysis
Yanina Gimenez, Guido Giussani
Braz. J. Probab. Stat. 32(4): 730-754 (November 2018). DOI: 10.1214/17-BJPS361

Abstract

In this article, we introduce a procedure for selecting variables in principal components analysis. It is developed to identify a small subset of the original variables that best explain the principal components through nonparametric relationships. There are usually some noisy uninformative variables in a dataset, and some variables that are strongly related to one another because of their general dependence. The procedure is designed to be used following the satisfactory initial principal components analysis with all variables, and its aim is to help to interpret the underlying structures. We analyze the asymptotic behavior of the method and provide some examples.

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Yanina Gimenez. Guido Giussani. "Searching for the core variables in principal components analysis." Braz. J. Probab. Stat. 32 (4) 730 - 754, November 2018. https://doi.org/10.1214/17-BJPS361

Information

Received: 1 March 2015; Accepted: 1 April 2017; Published: November 2018
First available in Project Euclid: 17 August 2018

zbMATH: 06979598
MathSciNet: MR3845027
Digital Object Identifier: 10.1214/17-BJPS361

Rights: Copyright © 2018 Brazilian Statistical Association

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Vol.32 • No. 4 • November 2018
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