Bernoulli

  • Bernoulli
  • Volume 19, Number 3 (2013), 954-981.

Empirical likelihood approach to goodness of fit testing

Hanxiang Peng and Anton Schick

Full-text: Open access

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.

Article information

Source
Bernoulli, Volume 19, Number 3 (2013), 954-981.

Dates
First available in Project Euclid: 26 June 2013

Permanent link to this document
https://projecteuclid.org/euclid.bj/1372251149

Digital Object Identifier
doi:10.3150/12-BEJ440

Mathematical Reviews number (MathSciNet)
MR3079302

Zentralblatt MATH identifier
1273.62103

Keywords
estimated constraint functions infinitely many constraints nuisance parameter regression model testing for a parametric model testing for a specific distribution testing for independence testing for symmetry

Citation

Peng, Hanxiang; Schick, Anton. Empirical likelihood approach to goodness of fit testing. Bernoulli 19 (2013), no. 3, 954--981. doi:10.3150/12-BEJ440. https://projecteuclid.org/euclid.bj/1372251149


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