Skip to main content

Research Repository

Advanced Search

Robustness of nonparametric predictive inference for future order statistics

Alqifari, H.N.; Coolen, F.P.A.

Robustness of nonparametric predictive inference for future order statistics Thumbnail


Authors

H.N. Alqifari



Abstract

This paper considers robustness of Nonparametric Predictive Inference (NPI), in particular considering inference involving future order statistics. The concept of robust inference is usually aimed at development of inference methods which are not too sensitive to data contamination or to deviations from model assumptions. In this paper we use it in a slightly narrower sense. For our aims, robustness indicates insensitivity to small change in the data, as our predictive probabilities for order statistics and statistical inferences involving future observations depend upon the given observations. We introduce some concepts for assessing the robustness of statistical procedures to the NPI framework, namely sensitivity curve and breakdown point; these classical concepts require some adoption for application in NPI. Most of our nonparametric inferences have a reasonably good robustness with regard to small changes in the data.

Citation

Alqifari, H., & Coolen, F. (2019). Robustness of nonparametric predictive inference for future order statistics. Journal of statistical theory and practice, 13(1), Article 12. https://doi.org/10.1007/s42519-018-0011-x

Journal Article Type Article
Acceptance Date Aug 15, 2018
Online Publication Date Oct 29, 2018
Publication Date Mar 31, 2019
Deposit Date Aug 17, 2018
Publicly Available Date Mar 28, 2024
Journal Journal of Statistical Theory and Practice
Electronic ISSN 1559-8616
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 13
Issue 1
Article Number 12
DOI https://doi.org/10.1007/s42519-018-0011-x

Files


Published Journal Article (2.1 Mb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© The Author(s) 2018.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.





You might also like



Downloadable Citations