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Empirical distribution function under heteroscedasticity
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SYSNO ASEP 0365534 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Empirical distribution function under heteroscedasticity Author(s) Víšek, Jan Ámos (UTIA-B) ORCID Number of authors 1 Source Title Statistics - ISSN 0233-1888
Roč. 45, č. 5 (2011), s. 497-508Number of pages 12 s. Language eng - English Country US - United States Keywords Robustness ; Convergence ; Empirical distribution ; Heteroscedasticity Subject RIV BB - Applied Statistics, Operational Research CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000299733400005 EID SCOPUS 80052687193 DOI 10.1080/02331881003768891 Annotation Neglecting heteroscedasticity of error terms may imply a wrong identification of regression. Employment of (heteroscedasticity resistent) White’s estimator of covariance matrix of estimates of regression coefficients may lead to the correct decision about significance of individual explanatory variables under heteroscedasticity. However, White’s estimator of covariance matrix was established for LS-regression analysis (in the case when error terms are normally distributed, LS- and ML-analysis coincide and hence then White’s estimate of covariance matrix is available for ML-regression analysis, too). To establish White’s-type estimate for another estimator of regression coefficients requires Bahadur representation of the estimator in question, under heteroscedasticity of error terms. The derivation of Bahadur representation for other (robust) estimators requires some tools. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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