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Content available remote Data driven tests for univariate symmetry
EN
We propose new data driven score rank tests for univariate symmetry around a known center.We apply both Schwarz-type and recently introduced data driven penalty selection rules. Some key asymptotic results regarding the test statistics are given and some asymptotic optimality properties proved. In an extensive simulation study, we compare the empirical behaviour of these tests to tests found in the recent literature to be powerful. We show that, for a broad range of asymmetric distributions, data driven tests have stable power, which is comparable to their competitors for typical alternatives and much greater for some atypical alternatives.
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Content available remote Data driven score tests for univariate symmetry based on non-smooth functions
EN
We propose data driven score rank tests for univariate symmetry around a known center based on non-smooth functions. A choice of non-smooth functions is motivated by very special properties of a certain function on [0; 1] determined by a distribution which is responsible for its asymmetry. We modify recently introduced data driven penalty selection rules and apply Schwarz-type penalty as well. We prove basic asymptotic results for the test statistics. In a simulation study we compare the empirical behavior of the new tests with the data driven tests based on the Legendre basis and with the so-called hybrid test. We show good power behavior of the new tests often overcoming their competitors.
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