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The Predictive Accuracy of Feed Forward Neural Networks and Multiple Regression in the Case of Heteroscedastic Data

DSpace at IIT Bombay

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Field Value
 
Title The Predictive Accuracy of Feed Forward Neural Networks and Multiple Regression in the Case of Heteroscedastic Data
 
Creator PALIWAL, M
KUMAR, UA
 
Subject heteroscedasticity
monte carlo simulation
neural network
regression analysis
 
Description During the last few years, several comparative studies for regression analysis and neural networks have been published. Our paper contributes to this stream of research by comparing the performance of feed forward neural network and multiple regression when heteroscedasticity is present in the data. Datasets are simulated that vary systematically on various dimensions like sample size, noise levels and number of independent variables to assess the consequences of deviations from underlying assumptions of homoscedasticity on the comparative performance of regression analysis and neural networks. Comparative analysis is carried out using appropriate experimental design and the results are presented.
 
Publisher IEEE
 
Date 2011-07-31T12:38:34Z
2011-12-26T12:53:01Z
2011-12-27T05:40:05Z
2011-07-31T12:38:34Z
2011-12-26T12:53:01Z
2011-12-27T05:40:05Z
2008
 
Type Article
 
Identifier IEEM: 2008 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-3, (), 430-434
http://dspace.library.iitb.ac.in/xmlui/handle/10054/8123
http://hdl.handle.net/10054/8123
 
Language en