Bootstrap techniques for massive data

Oct 7 (Wednesday) at 1:30 pm
BH 232M

Speaker: Jing Lei

Abstract: I will present the paper "A subsampled double bootstrap for massive data" by Srijan Sengupta, Stanislav Volgushev and Xiaofeng Shao. The motivation is to make bootstrap more computationally efficient for massive data with constraints on computation time, communication between distributed nodes, and storage. I will also cover related works in a unified framework, including the more classical "m out of n" bootstrap, the double bootstrap, and the more recent "bag of little bootstrap" . Although it would be a non-trivial achievement to just get the notation clear, I will also try to talk a little bit about the technical proofs if time permits.