Chapter 2: Frontier versus Ordinary Regression Models for Data Mining

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Marvin D. Troutt, Michael Hu, Murali Shanker and William Acar Kent State University, USA

Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

Abstract

Frontier Regression Models seek to explain boundary, frontier or optimal behavior rather than average behavior as in ordinary regression models. Ordinary regression is one of the most important tools for data mining. Frontier models may be desirable alternatives in many circumstances. In this chapter, we discuss frontier regression models and compare their interpretations to ordinary regression models. Occasional contact with stochastic frontier estimation models is also made, but we concentrate primarily on pure ceiling or floor frontier models. We also propose some guidelines for when to choose between them.

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