Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/114421
Title: | Nonparametric inference about increasing odds rate distributions | Authors: | Lando, Tommaso Arab, Idir Oliveira, Paulo Eduardo |
Keywords: | Hazard rate; heavy tails; nonparametric test; odds | Issue Date: | 2023 | Publisher: | Taylor & Francis | Project: | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB/00324/2020 | Serial title, monograph or event: | Journal of Nonparametric Statistics | Abstract: | To improve nonparametric estimates of lifetime distributions, we propose using the increasing odds rate (IOR) model as an alternative to other popular, but more restrictive, ‘adverse ageing’ models, such as the increasing hazard rate one. This extends the scope of applicability of some methods for statistical inference under order restrictions, since the IOR model is compatible with heavy-tailed and bathtub distributions. We study a strongly uniformly consistent estimator of the cumulative distribution function of interest under the IOR constraint. Numerical evidence shows that this estimator often outperforms the classic empirical distribution function when the underlying model does belong to the IOR family. We also study two different tests to detect deviations from the IOR property and establish their consistency. The performance of these tests is also evaluated through simulations. | URI: | https://hdl.handle.net/10316/114421 | ISSN: | 1048-5252 1029-0311 |
DOI: | 10.1080/10485252.2023.2220050 | Rights: | openAccess |
Appears in Collections: | I&D CMUC - Artigos em Revistas Internacionais FCTUC Matemática - Artigos em Revistas Internacionais |
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Nonparametric-inference-about-increasing-odds-rate-distributionsJournal-of-Nonparametric-Statistics.pdf | 2.22 MB | Adobe PDF | View/Open |
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