Cost estimation predictive modeling: regression versus neural network.: An article from: Engineering Economist
Book Details
Author(s)Alice E. Smith, Anthony K. Mason
ISBN / ASINB00097MMGO
ISBN-13978B00097MMG8
AvailabilityAvailable for download now
Sales Rank15,171,284
MarketplaceUnited States 🇺🇸
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This digital document is an article from Engineering Economist, published by Institute of Industrial Engineers, Inc. (IIE) on January 1, 1997. The length of the article is 6411 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: 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.
Citation Details
Title: Cost estimation predictive modeling: regression versus neural network.
Author: Alice E. Smith
Publication:Engineering Economist (Refereed)
Date: January 1, 1997
Publisher: Institute of Industrial Engineers, Inc. (IIE)
Volume: v42 Issue: n2 Page: p137(25)
Distributed by Thomson Gale
From the author: 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.
Citation Details
Title: Cost estimation predictive modeling: regression versus neural network.
Author: Alice E. Smith
Publication:Engineering Economist (Refereed)
Date: January 1, 1997
Publisher: Institute of Industrial Engineers, Inc. (IIE)
Volume: v42 Issue: n2 Page: p137(25)
Distributed by Thomson Gale
