Frontier Versus Ordinary Regression Models for Data Mining

Frontier Versus Ordinary Regression Models for Data Mining

Marvin D. Troutt (Kent State University, USA), Michael Hu (Kent State University, USA), Murali Shanker (Kent State University, USA) and William Acar (Kent State University, USA)
DOI: 10.4018/978-1-59140-057-8.ch002
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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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