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Mathematical Sciences

S3RI Seminar - "Univariate Mean Change Point Detection: Penalization, CUSUM and Optimality", Dr Yi Yu (University of Bristol) Seminar

S3RI Seminar
14:00 - 15:00
14 February 2019
Building 54, Room 7035, Lecture Theatre 7B, School of Mathematical Sciences, University of Southampton, Highfield Campus, SO17 1BJ

For more information regarding this seminar, please email Dr Helen Ogden at .

Event details

The problem of univariate mean change point detection and localization based on a sequence of $n$ independent observations with piecewise constant means has been intensively studied for more than half century, and serves as a blueprint for change point problems in more complex settings. We provide a complete characterization of this classical problem in a general framework in which the upper bound on the noise variance $\sigma^2$, the minimal spacing $\Delta$ between two consecutive change points and the minimal magnitude of the changes $\kappa$, are allowed to vary with $n$. We first show that consistent localization of the change points when the signal-to-noise ratio $\frac{\kappa \sqrt{\Delta}}{\sigma}$ is uniformly bounded from above is impossible. In contrast, when $\frac{\kappa \sqrt{\Delta}}{\sigma}$ is diverging in $n$ at any arbitrary slow rate, we demonstrate that two computationally-efficient change point estimators, one based on the solution to an $\ell_0$-penalized least squares problem and the other on the popular WBS algorithm, are both consistent and achieve a localization rate of the order $\frac{\sigma^2}{\kappa^2} \log(n)$. We further show that such rate is minimax optimal, up to a $\log(n)$ term.

Speaker information

Dr Yi Yu, University of Bristol. Dr Yu's research interests include: High-dimensional statistics; network studies; survival analysis; and applications in brain imaging data.

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