SPEAKER: Larry Wasserman; Carnegie Mellon University.
TITLE: “Assumption Free Predictive Inference”
ABSTRACT: Most work on high-dimensional inference uses strong assumptions such as linearity, incoherence, sparsity and constant variance. We consider inference, from a predictive point of view, without any assumptions except exchangeability. We start with high-dimensional regression. First we show that the bootstrap is very inaccurate which motivates moving away from the usual focus on regression parameters. Instead we focus on predictive quantities. In particular, we show that a class of methods called `conformal prediction' are very accurate under essentially no assumptions. Time permitting, we also discuss clustering and random effects from a predictive, assumption-free point of view. This is joint work with many collaborators.
DATE: Thursday, January 10th, 4:10pm
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS: 3:30pm MSB 4110 (4th floor lounge)
STA 290 Seminar List: https://statistics.ucdavis.edu/seminars