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Testing Heteroscedasticity in Robust Regression
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SYSNO ASEP 0377392 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Ostatní články Title Testing Heteroscedasticity in Robust Regression Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID Source Title Research Journal of Economics, Business and ICT - ISSN 2045-3345
Roč. 1, č. 4 (2011), s. 25-28Number of pages 4 s. Language eng - English Country GB - United Kingdom Keywords robust regression ; heteroscedasticity ; regression quantiles ; diagnostics Subject RIV BB - Applied Statistics, Operational Research CEZ AV0Z10300504 - UIVT-O (2005-2011) Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013 Electronic address http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf
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