Model selection in change-point problems
- Date: Friday 3 May 2019, 14:00 – 15:00
- Location: Mathematics Level 8, MALL 1, School of Mathematics
- Type: Seminars, Statistics
- Cost: Free
Dr Haeran Cho, University of Bristol (part of the Statistics Seminar Series)
Lately, there has been a surge of interest in research for computationally fast and statistically efficient methods for change-point detection, as nonstationarities frequently observed in real-life datasets are often attributed to structural breaks in the underlying stochastic properties. In multiple change-point detection, model selection via estimating the total number of change-points poses as a challenge, particularly when the dimensionality of the data is large. In this talk, I will address the model selection in change-point problems in two different settings: when $p = 1$ where one can benefit from localised application of an information criterion, and when $p$ is large where the change-point detection problem can be translated to that of detecting pervasive and latent 'factors'.