Coolen, F.P.A. and Bin Himd, S. (2020) 'Nonparametric predictive inference bootstrap with application to reproducibility of the two-sample Kolmogorov-Smirnov test.', Journal of statistical theory and practice., 14 (2). p. 26.
Abstract
This paper introduces a new bootstrap method based on the nonparametric predictive inference (NPI) approach to statistics. NPI is a frequentist statistics framework which explicitly focuses on prediction of future observations. The NPI framework enables a bootstrap method (NPI-B) to be introduced which, different to Efron’s classical bootstrap (Ef-B), is aimed at prediction of future observations instead of estimation of population characteristics. A brief initial comparison of NPI-B and Ef-B is presented. The main reason for introducing NPI-B here is for its application to NPI for reproducibility of statistical tests, which is illustrated for the two-sample Kolmogorov–Smirnov test.
| Item Type: | Article |
|---|---|
| Full text: | Publisher-imposed embargo (AM) Accepted Manuscript File format - PDF (254Kb) |
| Full text: | (VoR) Version of Record Available under License - Creative Commons Attribution. Download PDF (760Kb) |
| Status: | Peer-reviewed |
| Publisher Web site: | https://doi.org/10.1007/s42519-020-00097-5 |
| Publisher statement: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Date accepted: | 28 March 2020 |
| Date deposited: | 31 March 2020 |
| Date of first online publication: | June 2020 |
| Date first made open access: | 07 May 2020 |
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