Partially reduced-rank multivariate regression models

Gregory C. Reinsel, Raja P. Velu

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

A multivariate subset (or 'partially') reduced-rank regression model is considered as an extension of the usual multivariate reduced-rank model. In the model, the reduced-rank coefficient structure is specified to occur for a subset of the response variables only, which allows for more general situations and can lead to more efficient modeling than the usual reduced-rank model. The maximum likelihood estimation of parameters, likelihood ratio testing for rank, and large sample properties of estimators for this partially reduced-rank model are developed. An empirical procedure to aid in identification of the possible subset reduced-rank structure is suggested. Two numerical examples are examined to illustrate the methodology for the proposed model.

Original languageEnglish (US)
Pages (from-to)899-917
Number of pages19
JournalStatistica Sinica
Volume16
Issue number3
StatePublished - Jul 1 2006

Keywords

  • Canonical correlations
  • Covariance adjustment
  • Likelihood ratio test
  • Maximum likelihood estimator
  • Partially reduced-rank regression
  • Partitioned coefficient matrix

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Partially reduced-rank multivariate regression models'. Together they form a unique fingerprint.

  • Cite this