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Data driven tests for univariate symmetry

Inglot T., Janic A., Józefczyk J.

Probability and Mathematical Statistics

2012

Vol. 32, Fasc. 2

323--358

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.

Data driven score tests for univariate symmetry based on non-smooth functions

Józefczyk J.

Probability and Mathematical Statistics

2012

Vol. 32, Fasc. 2

301--322

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.

Data-driven score tests for homoscedastic linear regression model : asymptotic results

Inglot T., Ledwina T.

Probability and Mathematical Statistics

2006

Vol. 26, Fasc. 1

41--61

We describe and investigate new tests for testing the validity of a semiparametric random-design linear regression model. The tests were introduced in Inglot and Ledwina (2006a, b). We repeat here basic steps of the constructions. The resulting statistics are closely linked to some norms of the appropriate efficient score vector and related quantities. A useful way of deriving the efficient score vector is proposed and discussed. We introduce also a large class of estimators of the efficient score vector and prove that under the null model our constructions are asymptotically distribution free. The proof adopts and exploits some ideas and results developed in the area of semiparametric estimation. We give also the limiting distribution of the test statistics under the null hypothesis. The simulation results contained in Inglot and Ledwina (2006a, b) show the very good performance of the proposed tests.