Searching for the core variables in principal components analysis

被引：5
|
作者：
Gimenez, Yanina [1 ]
Giussani, Guido
机构：
[1] Univ San Andres, Vito Dumas 284, RA-1644 Buenos Aires, DF, Argentina
来源：
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS | 2018年 / 32卷 / 04期
关键词：
Informative variables; multivariate analysis; principal components; selection of variables; SELECTION; LASSO;
D O I：
10.1214/17-BJPS361
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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 non-parametric 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.
引用
收藏
页码：730 / 754
页数：25
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