Is Ridge Regression a Panacea.
Abstract
Consider the usual regression model y = Xbeta + epsilon where X is a matrix of full rank, beta is a vector of unknown parameters, and epsilon is a vector of random errors such that E(epsilon) = 0 and Var (epsilon) = (sigma squared)I. The procedure known as ridge regression has been offered as an alternative to ordinary least squares for estimating beta, particularly in those situations where severe multicollinearity exists in X. Ridge regression involves the use of a ridge estimator, which takes the form beta cap(k) = 1/(X'X + kI) X'y where k > or = 0. The properties of ridge regression relative to those of ordinary least squares are discussed. Although ridge regression does appear to offer promise, its use as a routine analysis method is not without shortcomings. Therefore, the question in the title is answered in the negative. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1977
- Accession Number
- ADA045138
Entities
People
- Dennis E. Smith