On multivariate ridge regression

Y Haitovsky

No 293092, University of Amsterdam, Actuarial Science and Econometrics Archive from University of Amsterdam, Faculty of Economics and Business

Abstract: A multivariate linear regression model with q responses as a linear function ofpindependent variables ry,= + k is considered withapxqparameter matrix B. The least squares (or Maximum Likelihood for multivariate normal E) % % estimator of B is deficient in that it takes no account of the % "across regression" correlations, on the one hand, and ignores the famous Stein effect, on the other hand. A remedy was offered by Brown and Zidek (1980) in the form of a multivariate ridge estimator. A richer class of estimators is obtained here by casting the model in a linear hierarchical framework, obtaining the Brown and Zidek multivariate ridge estimators., Efron and Morris' estimators of several normal mean vectors and Fearn's Bayesian estimators of growth curves as special cases. The unknown covariance cases result in an identifiability problem which is treated in a Bayesian fashion using conjugate priors. The method is then applied to forecasting the final election results from partial returns obtained at election night.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 32
Date: 1985-04
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/293092/files/amsterdam061.pdf (application/pdf)

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:ags:amstas:293092

DOI: 10.22004/ag.econ.293092

Access Statistics for this paper

More papers in University of Amsterdam, Actuarial Science and Econometrics Archive from University of Amsterdam, Faculty of Economics and Business Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().