Redundancy Index in Canonical Correlation Analysis with Linear Constraints

Akio Suzukawa and Nobuhiro Taneichi
Additional contact information
Akio Suzukawa: Obihiro University of Agriculture and Veterinary Medicine
Nobuhiro Taneichi: Obihiro University of Agriculture and Veterinary Medicine

A chapter in Measurement and Multivariate Analysis, 2002, pp 125-132 from Springer

Abstract: Summary The redundancy index proposed by Stewart and Love (1968) is an index to measure the degree to which one set of variables can predict another set of variables, and is associated with canonical correlation analysis. Yanai and Takane (1992) developed canonical correlation analysis with linear constraints (CCALC). In this paper we define a redundancy index in CCALC, which is based on the reformulation of CCALC by Suzukawa (1997). The index is a general measure to summarize redundancy between two sets of variables in the sense that various dependency measures can be obtained by choosing constraints suitably. The asymptotic distribution of the index is derived under normality.

Keywords: Asymptotic Distribution; Linear Constraint; Canonical Correlation; Canonical Correlation Analysis; Canonical Variate (search for similar items in EconPapers)
Date: 2002
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-4-431-65955-6_13

Ordering information: This item can be ordered from
http://www.springer.com/9784431659556

DOI: 10.1007/978-4-431-65955-6_13

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().