Cost estimation predictive modeling: Regression versus neural network Export

@article{citeulike:3738179, abstract = {Abstract: 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. 1.}, author = {Smith, Alice E. and Smith, Alice E. and Mason, Anthony K. and Mason, Anthony K.}, citeulike-article-id = {3738179}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.3314}, journal = {The Engineering Economist}, keywords = {neuralnetworks, regression, valuation}, pages = {137--161}, posted-at = {2008-12-02 23:45:25}, priority = {2}, title = {Cost estimation predictive modeling: Regression versus neural network}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.3314}, volume = {42}, year = {1997} }

TY - JOUR ID - citeulike:3738179 L3 - citeulike-article-id:3738179 N2 - Abstract: 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. 1. JF - The Engineering Economist EP - 161 TI - Cost estimation predictive modeling: Regression versus neural network VL - 42 SP - 137 KW - neuralnetworks KW - regression KW - valuation AU - Smith, Alice AU - Smith, Alice AU - Mason, Anthony AU - Mason, Anthony PY - 1997/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.3314 ER -