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Constrained Regression Estimates of Technology Effects on Fuel Economy

Summary: [This abstract is based on the authors' abstract.] Practical aspects of linear regression modeling are reviewed, explicitly incorporating knowledge about the signs and ordering of the regression parameters. Statistical properties of equality and inequality constrained estimators are presented. This constrained methodology is applied to the problem of estimating the potential impact of technologies on motor vehicle fuel economy within a database of over 2,000 model year 1988-1990 cars and trucks. These estimates are compared with those obtained from an ordinary least squares analysis and from a Bayesian analysis. The computational aspects of constrained least squares estimators are discussed. The problem can be formulated as a quadratic programming problem, transformed to a linear complementarity problem, and subsequently solved using Statistical Analysis System software. Depending on the nature of the constraints, the problem may be solved using a non-linear regression model. The transformations and mappings to accommodate these approaches are described.

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  • Topics:
  • Keywords: Bayesian methods,Regression analysis,Automobile industry,Least squares
  • Author: Gibbons, Diane I.; McDonald, Gary C.
  • Journal: Journal of Quality Technology