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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unreviewed preprint introducing CLOCC, a novel distribution-free statistical method for constructing lower confidence bounds on the number of changepoints in ordered sequences. The authors prove an impossibility result for upper bounds and demonstrate that their conformal p-value approach is universal for lower bounds under exchangeability and independence assumptions. Practical utility is shown through synthetic and real-data examples, but the work requires peer review and external validation before clinical or operational adoption.
Preprint. Intervention: Conformal Lower bound on Changepoint Count (CLOCC) method for constructing lower confidence bounds on K using conformal p-values..
Any distribution-free upper confidence bound on the number of changepoints K must be trivial and uninformative (impossibility result). CLOCC provides finite-sample valid lower confidence bounds on K using conformal p-values under exchangeability and independence assumptions. CLOCC is proven to be the only feasible approach (universality property) for providing lower bounds on K under stated assumptions.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is an unreviewed preprint presenting a novel statistical method for changepoint detection with theoretical proofs and empirical validation, but lacks peer review and clinical application.
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Suppose we are given an ordered sequence of independent data whose distribution changes $K$ times at unknown locations, for some unknown $K \geq 0$. In this paper, we study the problem of performing distribution-free inference on $K$. First, we show an impossibility result: any distribution-free upper confidence bound on $K$ must be trivial and uninformative. Then, using conformal $p$-values, and under only the assumption that the data segments induced by the changepoints are exchangeable (within themselves) and mutually independent, we construct a finite-sample valid lower confidence bound on $K$, which we call the Conformal LOwer bound on Changepoint Count (CLOCC). We show that CLOCC is the only feasible way to provide a lower bound on $K$ under the stated assumptions, a property we refer to as its universality. We provide practical guidelines for choosing score functions that yield efficient and tight lower bounds. We evaluate CLOCC in several synthetic and real-data experiments, where it provides informative lower bounds on $K$, demonstrating its practical applicability.
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