Statistical Science
- Statist. Sci.
- Volume 2, Number 1 (1987), 68-84.
Collinearity and Least Squares Regression
Abstract
In this paper we introduce certain numbers, called collinearity indices, which are useful in detecting near collinearities in regression problems. The coefficients enter adversely into formulas concerning significance testing and the effects of errors in the regression variables. Thus they provide simple regression diagnostics, suitable for incorporation in regression packages.
Article information
Source
Statist. Sci., Volume 2, Number 1 (1987), 68-84.
Dates
First available in Project Euclid: 19 April 2007
Permanent link to this document
https://projecteuclid.org/euclid.ss/1177013439
Digital Object Identifier
doi:10.1214/ss/1177013439
Mathematical Reviews number (MathSciNet)
MR896260
Zentralblatt MATH identifier
0643.62049
JSTOR
links.jstor.org
Keywords
Collinearity ill-conditioning linear regression errors in the variables regression diagnostics
Citation
Stewart, G. W. Collinearity and Least Squares Regression. Statist. Sci. 2 (1987), no. 1, 68--84. doi:10.1214/ss/1177013439. https://projecteuclid.org/euclid.ss/1177013439
See also
- See Comment: Donald W. Marquardt. [Collinearity and Least Squares Regression]: Comment. Statist. Sci., Volume 2, Number 1 (1987), 84--85.Project Euclid: euclid.ss/1177013440
- See Comment: David A. Belsley. [Collinearity and Least Squares Regression]: Comment: Well-Conditioned Collinearity Indices. Statist. Sci., Volume 2, Number 1 (1987), 86--91.Project Euclid: euclid.ss/1177013441
- See Comment: Ronald A. Thisted. [Collinearity and Least Squares Regression]: Comment. Statist. Sci., Volume 2, Number 1 (1987), 91--93.Project Euclid: euclid.ss/1177013442
- See Comment: Ali S. Hadi, Paul F. Velleman. [Collinearity and Least Squares Regression]: Comment: Diagnosing Near Collinearities in Least Squares Regression. Statist. Sci., Volume 2, Number 1 (1987), 93--98.Project Euclid: euclid.ss/1177013443
- See Comment: G. W. Stewart. [Collinearity and Least Squares Regression]: Rejoinder. Statist. Sci., Volume 2, Number 1 (1987), 98--100.Project Euclid: euclid.ss/1177013444
