Significance Regression: Improved Estimation from Collinear Data for the Measurement Error Model
- Creators
- Holcomb, Tyler R.
- Morari, Manfred
Abstract
This paper examines improved regression methods for the linear multivariable measurement error model (MEM) when the data suffers from "collinearity." The difficulty collinearity presents for reliable estinlation is discussed and a systematic procedure, significance regression (SR-MEM), is developed to address collinearity. In addition to mitigating collinearity difficulties SR-MEM produces asymptotically unbiased estimates. The use of ordinary least squares (OLS) for the MEM is examined. For collinear data OLS can improve the mean squared error of estimation over the maximum likelihood (ML) unbiased estimator in a manner analogous to ridge regression (RR). The significance regression method developed for the classical model (SR-classical) can also be used for data with measurement errors. SR-classical is similar SR-MEM and can yield better estimation than the ML estimator for collinear data. Numerical examples illustrate several points.
Additional Information
The author's thank Håkan Hjalmarsson for his helpful comments concerning asymptotic distributions. This research was partially supported by the Department of Energy, Office of Basic Energy Sciences, and by the Caltech Consortium in Chemistry and Chemical Engineering. Founding members of the Consortium are E. I. du Pont de Nemours and Company, inc., Eastman Kodak Company, Minnesota Mining and Manufacturing Company, and Shell Oil Company Foundation.Files
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Additional details
- Eprint ID
- 28047
- Resolver ID
- CaltechCDSTR:1993.004
- Created
-
2006-08-28Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field
- Caltech groups
- Control and Dynamical Systems Technical Reports