Statistics Seminar Speaker: Kengo Kato, 1/31/2018

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Wednesday Jan 31 2018

Statistics Seminar Speaker: Kengo Kato, 1/31/2018

4:15pm @ G01 Biotechnology

The Statistics Seminar speaker for Wednesday, January 31, 2018, will be Kengo Kato from the University of Tokyo. Kato's main research topic is the analysis of high-dimensional statistical and econometric models, specifically the estimation of the conditional quantile function when covariates are random functions, and quantile regression when the number of covariates is possibly (much) larger than the sample size. 

Talk: CLT and bootstrap for sample averages and incomplete U-statistics in high dimensions

Abstract: In modern statistical problems, there is a great interest in tractable distributional approximations to basic statistics such as sample averages and U-statistics where the dimensionality is possibly much larger than the sample size. 

In this talk, I will discuss some recent developments on Gaussian and bootstrap approximations in high dimensions. Specifically, in the first part, we will consider distributional approximations to the sum of independent random vectors, and show that under mild regularity conditions the Gaussian approximation holds uniformly on the hyperrectangles even when the dimension of the random vectors is much larger than the sample size. Building upon the Gaussian approximation result, we also establish finite sample validity of some bootstrap methods in high dimensions. 

In the second part, we will consider incomplete U-statistics. In general the computation of (standard) U-statistics tends to be prohibitively demanding even when the sample size and the dimension of the kernel are moderately large. To overcome such computational bottleneck, we shall employ randomized incomplete U-statistics with sparse weights whose computational cost can be made independent of the order of the kernel. Then we derive non-asymptotic Gaussian approximation error bounds for the incomplete U-statistics in high dimensions. In addition, we propose novel and generic bootstrap methods for the incomplete U-statistics that take computational considerations into account, and establish finite sample validity of the proposed bootstrap methods. 

Refreshments will be served following the seminar in 1181 Comstock Hall. 

PDF iconKato - Flyer.pdf