High-dimensional online changepoint detection and inference

Changepoint detection is a classical statistical problem, dating back at least to 1954 in the univariate case. However, modern applications in internet traffic monitoring, fMRI and finance, to name just a few, have motivated a resurgence of interest in the topic, from a high-dimensional perspective. One interesting model is where changes occur only in a sparse subset of coordinates (e.g. only a few voxels or a few stocks undergo a change in data generating mechanism), and the aim is to borrow strength across the different components to detect smaller changes than would be possible through only seeing any one of the individual series.

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This is an exciting joint position with @CambridgeCMIH ⏰ Application deadline is 19th April https://t.co/WapXM5SyMH View on Twitter