• Corpus ID: 219401662

A Note on Online Change Point Detection

@article{Yu2020ANO,
  title={A Note on Online Change Point Detection},
  author={Yi Yu and Oscar Hernan Madrid Padilla and Daren Wang and Alessandro Rinaldo},
  journal={arXiv: Statistics Theory},
  year={2020}
}
Online change point detection is originated in sequential analysis, which has been thoroughly studied for more than half century. A variety of methods and optimality results have been established over the years. In this paper, we are concerned with the univariate online change point detection problem allowing for all model parameters to change. We establish a theoretical framework to allow for more refined minimax results. This includes a phase transition phenomenon and a study of the detection… 
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7 Citations
Highly Influential Citations
1
Background Citations
4
Methods Citations
2

7 Citations

Sequential change point detection in high dimensional time series
TLDR
In a high dimensional scenario it is shown that the new monitoring schemes have asymptotic level alpha under the null hypothesis of no change and are consistent under the alternative of a change in at least one component.
A review on minimax rates in change point detection and localisation.
  • Yi Yu
  • Computer Science
  • 2020
TLDR
This paper starts with the univariate mean change point analysis problem and review the state-of-the-art results in the literature, then moves on to more complex data types and investigates general principles behind the optimal procedures that lead to minimax rate-optimal results.
Optimal network online change point localisation
TLDR
A minimax lower bound and two upper bounds based on NP-hard algorithms and polynomial-time algorithms, i.e. the detection delay, are established on the problem of online network change point detection.
Fast Online Changepoint Detection via Functional Pruning CUSUM statistics
TLDR
An algorithm, Functional Online CuSUM (FO-CuS), which is equivalent to running these earlier methods simultaneously for all sizes of window, or all possible values for the size of change, and gives tight bounds on the expected computational cost per iteration of FOCuS.
Dynamic and heterogeneous treatment effects with abrupt changes
From personalised medicine to targeted advertising, it is an inherent task to provide a sequence of decisions with historical covariates and outcome data. This requires understanding of both the…
Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates
TLDR
This work presents a simulation-based approach to setting time-varying thresholds that allows a desired expected runtime to be accurately targeted whilst additionally keeping the false positive rate constant across time steps and shows how the cost of using the popular quadratic time MMD estimator can be reduced.
Locally private online change point detection
TLDR
This work provides algorithms which respect the LDP constraint, which control the false alarm probability, and which detect changes with a minimal (minimax rate-optimal) delay, and presents the optimal rate in the benchmark, non-private setting.

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