STA 290 Seminar: Jing Lei

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Event Date

Mathematical Sciences Building 1147

Speaker: Jing Lei (Professor, Dept of Statistics and Data Science, Carnegie Mellon University)

Title: "A Conformal-Based Two-Sample Conditional Distribution Test"

Abstract: We consider the problem of testing the equality of the conditional distribution of a response variable given a set of covariates between two populations.  Such a testing problem is related to transfer learning and causal inference.  We develop a nonparametric procedure by combining recent advances in conformal prediction with some new ingredients such as a novel choice of conformity score and data-driven choices of weight and score functions. To our knowledge, this is the first successful attempt of using conformal prediction for testing statistical hypotheses beyond exchangeability. The final test statistic reveals a natural connection between conformal inference and the classical rank-sum test.  Our method is suitable for modern machine learning scenarios where the data has high dimensionality and the sample size is large, and can be effectively combined with existing classification algorithms to find good weight and score functions.  The performance of the proposed method is demonstrated in synthetic and real data examples.

Bio: Jing Lei is Professor at the Department of Statistics & Data Science, Carnegie Mellon University. His research interests include Model-free predictive inference; network data analysis; high dimensional multivariate analysis; functional data analysis; sequential Monte Carlo methods and state space models; data privacy. He is the recipient of the ASA Leo Breiman Junior Award, ASA Noether Young Scholar Award, and NSF CAREER Award. He is also an elected Fellow of the IMS. 

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Seminar Time/Location: Thursday Sept 22, 4:10pm, at Mathematical Sciences Building 1147