Robustness of nonparametric predictive inference for future order statistics

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

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: Feb 15, 2019
• 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
• Public URL: https://durham-repository.worktribe.com/output/1316988