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Activity Number: 637 - Non-Standard Regression Models
Type: Invited
Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #322007 View Presentation
Title: Super-Efficiency with the Divide and Conquer Method
Author(s): Moulinath Banerjee* and Cecile Durot and Bodhisattva Sen
Companies: University of Michigan and University of Paris Ouest and Columbia University
Keywords: divide and conquer ; pooled estimate ; cube root asymptotics

I will discuss how the divide and conquer method in non-standard problems (where natural estimates converge at non square root n rates and the limiting distributions are typically non-Gaussian) results in a super-efficiency phenomenon. I will focus mostly on problems with a cube-root convergence rate. For a fixed model, the asymptotic relative-efficiency of the divide-and-conquer-based pooled estimate to the global least squares estimate/global M-estimate goes to infinity, whereas its asymptotic relative efficiency in terms of maximal risk over a class of models (in a neighborhood of a fixed model) relative to the M-estimate converges to 0.

Authors who are presenting talks have a * after their name.

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