Criteria for selection of response variables and the asymptotic properties in a multivariate calibration

Ryuei Nishii

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Let a set of p responses y=(y 1,... y p )′ has a multivariate linear regression on a set of q explanatory variables x=(x 1,... x q )′. Our aim is to select the most informative subset of responses for making inferences about an unknown x from an observed y. Under normality on y, two selection methods, based on the asymptotic mean squared error and on the Akaike's information criterion, are proposed by Fujikoshi and Nishii (1986, Hiroshima Math. J., 16, 269-277). In this paper, under a mild condition we will derive the cross-validation criterion and obtain the asymptotic properties of the three procedures.

Original languageEnglish
Pages (from-to)319-329
Number of pages11
JournalAnnals of the Institute of Statistical Mathematics
Volume38
Issue number1
DOIs
Publication statusPublished - Dec 1 1986
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Fingerprint Dive into the research topics of 'Criteria for selection of response variables and the asymptotic properties in a multivariate calibration'. Together they form a unique fingerprint.

Cite this