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Piecewise Regression

Victor E. McGee and Willard T. Carleton
Journal of the American Statistical Association
Vol. 65, No. 331 (Sep., 1970), pp. 1109-1124
DOI: 10.2307/2284278
Stable URL: http://www.jstor.org/stable/2284278
Page Count: 16
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Piecewise Regression
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Abstract

A difficult regression parameter estimation problem is posed when the data sample is hypothesized to have been generated by more than a single regression model. To find the best-fitting number and location of underlying regression systems, the investigator must specify both the statistical criterion and the search-estimation procedure to be used. The approach outlined in this article is essentially a wedding of hierarchical clustering and standard regression theory. As the name suggests, piecewise regression may be described as a method of finding that piecewise continuous function which best describes the data sample. Computational procedures and a fully-worked example, together with possible extensions, are provided.

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