Brazilian Journal of Probability and Statistics

Searching for the core variables in principal components analysis

Yanina Gimenez and Guido Giussani

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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.

Article information

Braz. J. Probab. Stat., Volume 32, Number 4 (2018), 730-754.

Received: March 2015
Accepted: April 2017
First available in Project Euclid: 17 August 2018

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Zentralblatt MATH identifier

Informative variables multivariate analysis principal components selection of variables


Gimenez, Yanina; Giussani, Guido. Searching for the core variables in principal components analysis. Braz. J. Probab. Stat. 32 (2018), no. 4, 730--754. doi:10.1214/17-BJPS361.

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