COST ESTIMATION PREDICTIVE MODELING: REGRESSION VERSUS NEURAL NETWORK
作者:
ALICEE. SMITH,
ANTHONYK. MASON,
期刊:
The Engineering Economist
(Taylor Available online 1997)
卷期:
Volume 42,
issue 2
页码: 137-161
ISSN:0013-791X
年代: 1997
DOI:10.1080/00137919708903174
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Cost estimation generally involves predicting labor, material, utilities or other costs over time given a small subset of factual data on “cost drivers.” Statistical models, usually of the regression form, have assisted with this projection. Artificial neural networks are non-parametric statistical estimators, and thus have potential for use in cost estimation modeling. This research examined the performance, stability and ease of cost estimation modeling using regression versus neural networks to develop cost estimating relationships (CERs). Results show that neural networks have advantages when dealing with data that does not adhere to the generally chosen low order polynomial forms, or data for which there is little a priori knowledge of the appropriate CER to select for regression modeling. However, in cases where an appropriate CER can be identified, regression models have significant advantages in terms of accuracy, variability, model creation and model examination. Both simulated and actual data sets are used for comparison.
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