Empirical distribution function under heteroscedasticity

0365534 - ÚTIA 2012 RIV US eng J - Journal Article
Víšek, Jan Ámos
Empirical distribution function under heteroscedasticity.
Statistics. Roč. 45, č. 5 (2011), s. 497-508. ISSN 0233-1888. E-ISSN 1029-4910
Grant - others:GA UK(CZ) GA402/09/0557
Institutional research plan: CEZ:AV0Z10750506
Keywords : Robustness * Convergence * Empirical distribution * Heteroscedasticity
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 0.724, year: 2011
http://library.utia.cas.cz/separaty/2011/SI/visek-0365534.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0200758