The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression

This research explores the robustness of simple linear regression and artificial neural networks with respect to varying sample size and variance of the error term by comparing their predictive abilities. The comparison is made using the root mean square difference between the predicted output from...

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Bibliographic Details
Published in:Computers & operations research 1998-04, Vol.25 (4), p.251-263
Main Authors: Markham, Ina S., Rakes, Terry R.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Comparative studies
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Linear inference, regression
Mathematics
Neural networks
Operations research
Probability and statistics
Regression analysis
Sample size
Sciences and techniques of general use
Statistics
Variance analysis
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Summary:This research explores the robustness of simple linear regression and artificial neural networks with respect to varying sample size and variance of the error term by comparing their predictive abilities. The comparison is made using the root mean square difference between the predicted output from each technique and the actual output.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/S0305-0548(97)00074-9