University of Cambridge > > Isaac Newton Institute Seminar Series > Revisiting Huber’s M-Estimation: A Tuning-Free Approach

Revisiting Huber’s M-Estimation: A Tuning-Free Approach

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact INI IT.

This talk has been canceled/deleted

We introduce a novel scheme to choose the scale or robustification parameter in Huber’s method for mean estimation and linear regression in both low and high dimensional settings, which is tuning-free. For robustly estimating the mean of a univariate distribution, we first consider the adaptive Huber estimator with the robustification parameter calibrated via the censored equation approach. Our theoretical results provide finite sample guarantees for both the estimation and calibration parts. To further reduce the computational complexity, we next develop an alternating M-estimation procedure, which simultaneously estimates the mean and variance in a sequential manner. This idea can be naturally extended to regression problems in both low and high dimensions. We provide simple and fast algorithms to implement this procedure under various scenarios and study the numerical performance through simulations.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

This talk is not included in any other list

Note that ex-directory lists are not shown.


© 2006-2022, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity