*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
We consider M-estimates in a regression model where the noises are of unknown
but stochastically bounded distribution. An asymptotic minimax M-estimate is
derived. Simulations demonstrate the robustness of this approach, as well as
advantages over commonly used estimates such as ordinary least square estimate
and the Huber's estimate. The new method is named regression with tochastically
bounded noises (RSBN). We provide an iterative numerical solution, which is
derived from the proximal point method. The iterative method is elegant,
however may not have fast rate of convergence. RSBN can also be solved by
applying existing state-of-the-art nonlinear optimization software. We present
SNOPT as one example. Insights from RSBN are discussed. (Joint work with Xiaoming Huo).
