[Ml-stat-talks] [Released] Victor Chernozukov - Friday, Nov. 21, 12:30pm

Ramon van Handel rvan at Princeton.EDU
Wed Nov 19 18:52:20 EST 2014

Dear all,
The talk below should be of interest to many of you.
Best,  -- Ramon


This is a friendly reminder that on Nov. 21 (Friday) at 12:30 PM, we will have 
a talk by Victor Chernozhukov from MIT. The talk is in Sherrerd 101.

Title: Gaussian Approximations, Bootstrap, and Z-estimators when p >> n.

Abstract: We show that central limit theorems hold for high-dimensional
normalized means hitting high-dimensional rectangles. These results apply
even when p>> n. These theorems provide Gaussian distributional
approximations that are not pivotal, but they can be consistently estimated
via Gaussian multiplier methods and the empirical bootstrap. These results
are useful for building confidence bands and for multiple testing via the
step-down methods.  Moreover, these results hold for approximately linear
estimators. As an application we show that these central limit theorems
apply to normalized Z-estimators of p> n target parameter in a class of
problems, with estimating equations for each target parameter orthogonalized
with respect to the nuisance functions being estimated via sparse methods.
(This talk is based primarily on the joint work with Denis Chetverikov and
Kengo Kato.)

References:   arXiv:1212.6906 arXiv:1212.6885 arXiv:1304.0282 arXiv:1312.7614

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