Empirical likelihood approach to goodness of fit testing
Abstract
Motivated by applications to goodness of fit testing, the empirical likelihood approach is generalized to allow for the number of constraints to grow with the sample size and for the constraints to use estimated criteria functions. The latter is needed to deal with nuisance parameters. The proposed empirical likelihood based goodness of fit tests are asymptotically distribution free. For univariate observations, tests for a specified distribution, for a distribution of parametric form, and for a symmetric distribution are presented. For bivariate observations, tests for independence are developed.
Keywords
Cite
@article{arxiv.1307.5982,
title = {Empirical likelihood approach to goodness of fit testing},
author = {Hanxiang Peng and Anton Schick},
journal= {arXiv preprint arXiv:1307.5982},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.3150/12-BEJ440 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)