Introducing nonparametric predictive inference methods for reproducibility of likelihood ratio tests

Marques, F.J.; Coolen, F.P.A.; Coolen-Maturi, T.

doi:10.1007/s42519-018-0020-9

Introducing nonparametric predictive inference methods for reproducibility of likelihood ratio tests

Marques, F.J.; Coolen, F.P.A.; Coolen-Maturi, T.

Authors

F.J. Marques

Professor Frank Coolen frank.coolen@durham.ac.uk
Professor

Dr Tahani Coolen-Maturi tahani.maturi@durham.ac.uk
Associate Professor

Abstract

This paper introduces the nonparametric predictive inference approach for reproducibility of likelihood ratio tests. The general idea of this approach is outlined for tests between two simple hypotheses, followed by an investigation of reproducibility for tests between two beta distributions. The paper reports on the first steps of a wider research programme towards tests involving composite hypotheses and substantial computational challenges.

Citation

Marques, F., Coolen, F., & Coolen-Maturi, T. (2019). Introducing nonparametric predictive inference methods for reproducibility of likelihood ratio tests. Journal of statistical theory and practice, 13, Article 15. https://doi.org/10.1007/s42519-018-0020-9

Journal Article Type	Article
Acceptance Date	Oct 13, 2018
Online Publication Date	Oct 31, 2018
Publication Date	Mar 31, 2019
Deposit Date	Oct 15, 2018
Publicly Available Date	Oct 31, 2019
Journal	Journal of Statistical Theory and Practice
Electronic ISSN	1559-8616
Publisher	Springer
Peer Reviewed	Peer Reviewed
Volume	13
Article Number	15
DOI	https://doi.org/10.1007/s42519-018-0020-9

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Copyright Statement
This is a post-peer-review, pre-copyedit version of an article published in Journal of statistical theory and practice. The final authenticated version is available online at: https://doi.org/10.1007/s42519-018-0020-9