Nonparametric changepoint detection with sparse alternatives
Add to your list(s)
Download to your calendar using vCal
If you have a question about this talk, please contact Mustapha Amrani.
Inference for ChangePoint and Related Processes
We consider the problem of detecting the change in mean in a sequence of Gaussian vectors. We assume that the change happens only in some of the components of the vector. We construct a nonparametric testing procedure that is adaptive to the number of changing components. Under highdimensional assumptions we obtain the detection boundary and show the rate optimality of the test.
This talk is part of the Isaac Newton Institute Seminar Series series.
This talk is included in these lists:
Note that exdirectory lists are not shown.
