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Testing symmetry of unknown densities via smoothing with the generalized gamma kernels

Masayuki Hirukawa and Mari Sakudo
This paper improves a kernel-smoothed test of symmetry through combining it with a new class of asymmetric kernels called the generalized gamma kernels. It is demonstrated that the improved test statistic has a normal limit under the null of symmetry and is consistent under the alternative. A test-oriented smoothing parameter selection method is also proposed to implement the test. Monte Carlo simulations indicate superior finite-sample performance of the test statistic. It is worth emphasizing that the performance is grounded on the first-order normal limit and a small number of observations, despite a nonparametric convergence rate and a sample-splitting procedure of the test.
Year of publication:
June 2016
Authors: Hirukawa, Masayuki ; Sakudo, Mari
Published in:
Econometrics : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 4.2016, 2, p. 1-27
Subject: asymmetric kernel | degenerate U-statistic | generalized gamma kernels | nonparametric kernel testing | smoothing parameter selection | symmetry test | two-sample goodness-of-fit test | Nichtparametrisches Verfahren | Nonparametric statistics | Schätztheorie | Estimation theory | Statistischer Test | Statistical test | Core | Statistische Verteilung | Statistical distribution
Type of publication: Article
Type of publication (narrower categories): Aufsatz in Zeitschrift ; Article in journal
Language: English
Other identifiers:
10.3390/econometrics4020028 [DOI]
hdl:10419/171879 [Handle]
Classification: C12 - Hypothesis Testing ; C14 - Semiparametric and Nonparametric Methods
Source:
ECONIS - Online Catalogue of the ZBW
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011506402
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