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The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: exploration of some issues
Journal article   Peer reviewed

The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: exploration of some issues

P.N. SubbaNarasimha, B. Arinze and M. Anandarajan
Expert systems with applications, v 19(2), pp 117-123
2000
DOI: https://doi.org/10.1016/S0957-4174(00)00026-9

Abstract

Neural networks Regression Skewed data
Business organizations can be viewed as information-processing units making decisions under varying conditions of uncertainty, complexity, and fuzziness in the causal links between performance and various organizational and environmental factors. The development and use of appropriate decision-making tools has, therefore, been an important activity of management researchers and practitioners. Artificial neural networks (ANNs) are turning out to be an important addition to an organization's decision-making tool kit. A host of studies has compared the efficacy of ANNs to that of multivariate statistical methods. Our paper contributes to this stream of research by comparing the relative performance of ANN and multiple regression when the data contain skewed variables. We report results for two separate data sets; one related to individual performance and the second to firm performance. The results are used to highlight some salient issues related to the use of ANN and multiple regression models in organizational decision-making.

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29 citations in Web of Science
28 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Operations Research & Management Science
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