# A likelihood ratio approach to sequential change point detection for a general class of parameters

arXiv: https://arxiv.org/abs/1802.07696 · HTML: https://mlsanity.com/paper/1802.07696/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [Optimal network online change point localisation](https://mlsanity.com/paper/2101.05477/) (2021)
- [Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens](https://mlsanity.com/paper/2311.11463/) (2023)
- [Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions](https://mlsanity.com/paper/2211.09781/) (2022)
- [Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures](https://mlsanity.com/paper/2410.22594/) (2024)
- [Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent](https://mlsanity.com/paper/2405.12553/) (2024)
- [Noise-contrastive Online Change Point Detection](https://mlsanity.com/paper/2206.10143/) (2022)
- [MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme](https://mlsanity.com/paper/2309.06739/) (2023)
- [Score-based change point detection via tracking the best of infinitely many experts](https://mlsanity.com/paper/2408.14073/) (2024)

## Abstract (arXiv)

In this paper we propose a new approach for sequential monitoring of a parameter of a d-dimensional time series, which can be estimated by approximately linear functionals of the empirical distribution function. We consider a closed-end-method, which is motivated by the likelihood ratio test principle and compare the new method with two alternative procedures. We also incorporate self-normalization such that estimation of the long-run variance is not necessary. We prove that for a large class of testing problems the new detection scheme has asymptotic level α and is consistent. The asymptotic theory is illustrated for the important cases of monitoring a change in the mean, variance and correlation. By means of a simulation study it is demonstrated that the new test performs better than the currently available procedures for these problems.Finally the methodology is illustrated by a small data example investigating index prices from the dot-com bubble.