# Simultaneous Detection and Localization of Mean and Covariance Changes in High Dimensions

arXiv: https://arxiv.org/abs/2508.19523 · HTML: https://mlsanity.com/paper/2508.19523/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2025.

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

- [Optimal detection of general moment changes: Simultaneous mean and covariance change detection and beyond](https://mlsanity.com/paper/2609.36594/) (2026)

## Abstract (arXiv)

Existing methods for high-dimensional changepoint detection and localization typically focus on changes in either the mean vector or the covariance matrix separately. This separation reduces detection power and localization accuracy when both parameters change simultaneously. We propose a simple yet powerful method that jointly monitors shifts in both the mean and covariance structures. Under mild conditions, the test statistics for detecting these shifts jointly converge in distribution to a bivariate standard normal distribution, revealing their asymptotic independence. This independence enables the combination of the individual p-values using Fisher's method, and the development of an adaptive p-value-based estimator for the changepoint. Theoretical analysis and extensive simulations demonstrate the superior performance of our method in terms of both detection power and localization accuracy.