Minimax Ridge Regression.
Abstract
This work examined minimax linear estimation in multiple linear regression. The application of minimax estimation to regression led to the development of ridge regression estimators with stochastic ridge parameters. These estimators were seen to be invariant under linear transformation; a property which has not been established for other ridge estimators. These minimax-motivated estimators were examined in several simulation studies. In particular, flaws in other simulation studies of ridge estimators were depicted. Consequently, an improved simulation procedure was used. It was observed from these studies that, contrary to published statements, a ridge estimator can be considerably superior to the ordinary least squares estimator, especially when high pairwise correlations exist among the regression variables. Robustness considerations were used to suggest a requirement that a 'good' generalized ridge regression estimator should satisfy. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1980
- Accession Number
- ADA100146
Entities
People
- Lawrence C. Peele
- Thomas P. Ryan
Organizations
- Old Dominion University