Testing the Hypothesis of a Homoscedastic Error Term in Simple, Nonparametric Regression

Autor/in	Wilcox, Rand R.
Titel	Testing the Hypothesis of a Homoscedastic Error Term in Simple, Nonparametric Regression
Quelle	In: Educational and Psychological Measurement, 66 (2006) 1, S.85-92 (8 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164405278578
Schlagwörter	Nonparametric Statistics; Mathematical Models; Regression (Statistics); Probability; Hypothesis Testing; Research Methodology + Suchen Sie Ihr Suchwort? Mathematical model; Mathematisches Modell; Regression; Regressionsanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Hypothesenprüfung; Hypothesentest; Research method; Forschungsmethode
Abstract	Consider the nonparametric regression model Y = m(X)+ [tau](X)[epsilon], where X and [epsilon] are independent random variables, [epsilon] has a median of zero and variance [sigma][squared], [tau] is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated with Y, given X. This article considers the problem of testing H[0]:[tau] = 1, the hypothesis that the error term is homoscedastic. Several methods were considered, two of which were found that control the probability of a Type I error well in simulations. One is fast from a computational point of view, and the other is based in part on a bootstrap method. Neither dominates in terms of power. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10