# Non-partitioned e-detectors for nonparametric sequential change detection

arXiv: https://arxiv.org/abs/2607.28322 · HTML: https://mlsanity.com/paper/2607.28322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [Reducing sequential change detection to sequential estimation](https://mlsanity.com/paper/2309.09111/) (2023)
- [Asymptotically optimal sequential change detection for bounded means](https://mlsanity.com/paper/2602.05272/) (2026)
- [Power one sequential tests exist for weakly compact mathscr P against mathscr Pc](https://mlsanity.com/paper/2604.03218/) (2026)
- [Data-Driven Quickest Change Detection in (Hidden) Markov Models](https://mlsanity.com/paper/2210.11988/) (2022)
- [Sequential Change Detection Under Markov Setup With Unknown Prechange And Postchange Distributions](https://mlsanity.com/paper/2510.26204/) (2025)

## Built on by (most cited first, 1 of 1)

- [A complete characterization of sequential testability and change detectability in i.i.d. models](https://mlsanity.com/paper/2609.05752/) (2026)

## Abstract (arXiv)

We study the problem of sequential change detection over a general class of probability distributions (mathcal P), where both the pre-change and post-change distributions are unknown and belong to mathcal P. We do not assume a pre-specified partition of mathcal P into pre- and post-change families. We propose a general class of sequential change detectors obtained by aggregating point-null e-processes over possible changepoints and taking an infimum over candidate no-change distributions. The weights in the aggregation scheme determine whether they attain average run length (ARL) control and probability-of-false-alarm (PFA) control. Under suitable assumptions, we prove that our methods achieve first-order asymptotically optimal detection delay. Concrete examples include sub-Gaussian and bounded mean changes, Gaussian mean changes with unknown variance, as well as changes in Markov transition matrices.