Academic Journal
Tolerance intervals in statistical software and robustness under model misspecification
| Title: | Tolerance intervals in statistical software and robustness under model misspecification |
|---|---|
| Authors: | Kyung Serk Cho, Hon Keung Tony Ng |
| Source: | Journal of Statistical Distributions and Applications. 8 |
| Publisher Information: | Springer Science and Business Media LLC, 2021. |
| Publication Year: | 2021 |
| Subject Terms: | Statistical tolerance regions, Tolerance (Engineering)--Statistical methods, 13. Climate action, 0211 other engineering and technologies, Observed confidence levels (Statistics), 02 engineering and technology, 0101 mathematics, 01 natural sciences |
| Description: | A tolerance interval is a statistical interval that covers at least 100ρ%of the population of interest with a 100(1−α)%confidence, whereρandαare pre-specified values in (0, 1). In many scientific fields, such as pharmaceutical sciences, manufacturing processes, clinical sciences, and environmental sciences, tolerance intervals are used for statistical inference and quality control. Despite the usefulness of tolerance intervals, the procedures to compute tolerance intervals are not commonly implemented in statistical software packages. This paper aims to provide a comparative study of the computational procedures for tolerance intervals in some commonly used statistical software packages including JMP, Minitab, NCSS, Python, R, and SAS. On the other hand, we also investigate the effect of misspecifying the underlying probability model on the performance of tolerance intervals. We study the performance of tolerance intervals when the assumed distribution is the same as the true underlying distribution and when the assumed distribution is different from the true distribution via a Monte Carlo simulation study. We also propose a robust model selection approach to obtain tolerance intervals that are relatively insensitive to the model misspecification. We show that the proposed robust model selection approach performs well when the underlying distribution is unknown but candidate distributions are available. |
| Document Type: | Article Other literature type |
| Language: | English |
| ISSN: | 2195-5832 |
| DOI: | 10.1186/s40488-021-00123-2 |
| DOI: | 10.7916/qn3a-yr07 |
| Access URL: | https://jsdajournal.springeropen.com/track/pdf/10.1186/s40488-021-00123-2 https://jsdajournal.springeropen.com/articles/10.1186/s40488-021-00123-2 https://link.springer.com/content/pdf/10.1186/s40488-021-00123-2.pdf |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....662fb4b05da3c624a5ff2724ac2fa03d |
| Database: | OpenAIRE |
| ISSN: | 21955832 |
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| DOI: | 10.1186/s40488-021-00123-2 |