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Testing for Skew-Symmetric Models

Banff International Research Station for Mathematical Innovation and Discovery

Skew-symmetric (ss) models are semiparametric models for continuous random vectors. Starting with a symmetric probability density function (pdf), a ss family is generated by multiplying the symmetric pdf by a skewing function. A characterization of this skewing function let us propose a test for testing goodness of fit to a ss model. The test statistic is based on comparing two estimators of the population pdf: a consistent kernel-based estimator and another estimator which is built by assuming that the ss in the null hypothesis is true. The proposed test shares some properties with other tests based on comparing a kernel-based estimator of the population pdf to an estimator derived under the null hypothesis, like the ones in Hall (J. Multivar. Anal. 14, 1984, 1--16) and Fan (Econometric Theory, 10, 1994, 316--356), and other are new.

Mathematics; Statistics; Probability theory and stochastic processes

video/mp4

Author affiliation: University of Sevilla

2014-10-02

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/50544

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/