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Title	Testing for Skew-Symmetric Models
Creator	Jiménez-Gamero, María Dolores
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2013-05-20
Description	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.
Extent	27 minutes
Subject	Mathematics Statistics Probability theory and stochastic processes
Type	Moving Image
FileFormat	video/mp4
Language	eng
Notes	Author affiliation: University of Sevilla
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2014-10-02
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI	10.14288/1.0044024
URI	http://hdl.handle.net/2429/50544
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
AggregatedSourceRepository	DSpace

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