Principal component analysis of interval data: a symbolic data analysis approach

被引：74

作者：

Lauro, CN ^{[1
]}

Palumbo, F

机构：

[1] Univ Naples Federico II, Dipartimento Matemat & Stat, Naples, Italy

[2] Univ Macerata, Dipartimento Istituz Econ & Finanziarie, Macerata, Italy

来源：

COMPUTATIONAL STATISTICS | 2000年 / 15卷 / 01期

关键词：

principal components; interval data; symbolic objects;

D O I：

10.1007/s001800050038

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：

The present paper deals with the study of continuous interval data by means of suitable Principal Component Analyses (PCA). Statistical units described by interval data can be assumed as special cases of Symbolic Objects (SO) (Diday, 1987). In Symbolic Data Analysis (SDA), these data are represented as hypercubes. In the present paper, we propose some extensions of the PCA with the aim of representing, in a space of reduced dimensions, images of such hypercubes, pointing out differences and similarities according to their structural features.

引用

页码：73 / 87

页数：15