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Genetic algorithm optimization technique for linear regression models with heteroscedastic errors

Indian Agricultural Research Journals

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Title Genetic algorithm optimization technique for linear regression models with heteroscedastic errors
 
Creator IQUEBAL, M A
PRAJNESHU, PRAJNESHU
GHOSH, HIMADRI
 
Subject Genetic algorithm; Heteroscedasticity; Linear regression model; White’s general heteroscedasticity test
 
Description Most widely used statistical technique for estimating cause-effect relationships is the Linear regression methodology. Ordinary least squares (OLS) method, which is valid under certain assumptions, is generally used to estimate the underlying parameters. If the errors are not homoscedastic, OLS estimates lead to incorrect inferences. In this article, use of the powerful stochastic optimization technique of Genetic algorithm (GA) is advocated for estimation of regression parameters and variance parameter simultaneously even when nothing is known about the form of heteroscedasticity. Parametric bootstrap methodology is employed to obtain standard errors of the estimates. The methodology is illustrated by applying it to a dataset.
 
Publisher The Indian Journal of Agricultural Sciences
 
Contributor —
 
Date 2012-05-14
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/17802
 
Source The Indian Journal of Agricultural Sciences; Vol 82, No 5 (2012)
0019-5022
 
Language eng
 
Relation http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/17802/8577
 
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