Testing normality via a distributional fixed point property in the Stein characterization

We propose two families of tests for the classical goodness-of-fit problem to univariate normality. The new procedures are based on \(L^2\)-distances of the empirical zero-bias transformation to the normal distribution or the empirical distribution of the data, respectively. Weak convergence results...

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Bibliographic Details
Published in:arXiv.org 2018-03
Main Authors: Betsch, Steffen, Ebner, Bruno
Format: Article
Language:English
Subjects:
Goodness of fit
Normal distribution
Normality
Null hypothesis
Online Access:Get full text
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Summary:We propose two families of tests for the classical goodness-of-fit problem to univariate normality. The new procedures are based on \(L^2\)-distances of the empirical zero-bias transformation to the normal distribution or the empirical distribution of the data, respectively. Weak convergence results are derived under the null hypothesis, under fixed alternatives as well as under contiguous alternatives. Empirical critical values are provided and a comparative finite-sample power study shows the competitiveness to classical procedures.
ISSN:2331-8422
DOI:10.48550/arxiv.1803.07069