Conformal testing in a binary model situation
Not on the map. Why?
Abstract (arXiv)
Conformal testing is a way of testing the IID assumption based on conformal prediction. The topic of this note is computational evaluation of the performance of conformal testing in a model situation in which IID binary observations generated from a Bernoulli distribution are followed by IID binary observations generated from another Bernoulli distribution, with the parameters of the distributions and changepoint unknown. Existing conformal test martingales can be used for this task and work well in simple cases, but their efficiency can be improved greatly.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 4 of its 26 references that are in the map. First posted on arXiv 5 Apr 2021.
Builds on
4 of 26 references.
References · 4
- 2019Safe Testingcited by 98
- 2019Testing randomnesscited by 32
- 2021Retrain or not retrain: Conformal test martingales for change-point detectioncited by 28
- 2020Testing for concept shift onlinecited by 14
Built on by
Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 2.
4 papers.
- 2024Combining Evidence Across Filtrationscited by 2
- 2024Diagnostic Runtime Monitoring with Martingalescited by 2
- 2021Enhancement of prediction algorithms by bettingcited by 1
- 2021Conformal testing: binary case with Markov alternativescited by 0
- 2024Diagnostic Runtime Monitoring with Martingales31 Jul 2024
- 2024Combining Evidence Across Filtrations15 Feb 2024
- 2021Conformal testing: binary case with Markov alternatives2 Nov 2021
- 2021Enhancement of prediction algorithms by betting18 May 2021