Hi everyone!
Our speaker this Wednesday (10/22) at Applied Stats will be *Victor
Chernozhukov, *who will be practicing his job talk. Brandon will be giving
a talk entitled *Gaussian Approximations, Bootstrap, and Z-estimators when
p>>n. *The abstract for the talk is included below. As per usual, we will
meet in CGIS K354 at noon and lunch will be served.
I look forward to seeing you all there! Thank you!
-- Dana Higgins
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.)
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