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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/14416

Title: CONTRASTING NEURAL NETS WITH REGRESSION IN PREDICTING PERFORMANCE IN THE TRANSPORTATION INDUSTRY
Authors: Duliba, Katherine A.
Issue Date: Oct-1990
Publisher: Stern School of Business, New York University
Series/Report no.: IS-90-17
Abstract: The purpose of this paper is to compare and contrast traditional regression models with a neural network model, in order to predict performance in the transportation industry. No regression model has emerged as obviously superior in previous work conducted on predicting transportation performance. Therefore, a neural network model was investigated as an alternative to regression. It was found that a neural net model outperformed the corresponding random effects specification, but did not perform as well as the fixed effects specification.
URI: http://hdl.handle.net/2451/14416
Appears in Collections:IOMS: Information Systems Working Papers

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