Since 2014
Testing Heteroscedasticity In Robust Regression
Research Journal of Economics, Business and ICT
View Archive Info| Field | Value | |
| Title |
Testing Heteroscedasticity In Robust Regression
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| Creator |
Kalina, Jan; Institute of Computer Science of the Academy of Sciences
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| Subject |
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Heteroscedasticity, Robust regression C14, C12, C21 |
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| Description |
This work studies the phenomenon of heteroscedasticity and its consequences for various robust estimation methods for the linear regression, including the least weighted squares, regression quantiles and trimmed least squares estimators. We investigate hypothesis tests for these regression methods. and removing heteroscedasticity from the linear regression model. The new asymptotic heteroscedasticity tests for robust regression are asymptotically equivalent to standard tests computed for the least squares regression. Also we describe an asymptotic approximation to the exact null distribution of the test statistics. We describe a robust estimation procedure for the linear regression with heteroscedastic errors.
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| Publisher |
English Time Schools & Overseas Education
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| Contributor |
—
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| Date |
2012-03-10
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
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| Format |
application/pdf
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| Identifier |
http://ojs.journals.cz/index.php/RJEBI/article/view/242
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| Source |
Research Journal of Economics, Business and ICT; Vol 4 (2012)
2047-7848 2045-3345 |
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| Language |
eng
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| Relation |
http://ojs.journals.cz/index.php/RJEBI/article/view/242/246
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| Rights |
Copyright (c) 2012 Jan Kalina
https://creativecommons.org/licenses/by/3.0/ |
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