【报告摘要】We provide an introduction to label shift problems. In the context of discrete response, we study the importance weights confidence set problem by a paradigm shift from traditional inversion-based inference to a direct matrix constraint framework. We use this framework to characterize a joint confidence region and extract marginal intervals via linear programming, deriving provably tighter bounds for importance weights while maintaining exact finite-sample validity. In the context of continuous response, we study the estimation and inference of a general target population characteristric by developing doubly and singly robust estimators as well as the efficient estimator. Many ongoing and future developments will be discussed too.

 【报告人简介】Yanyuan Ma is a Professor of Statistics at Penn State. Ma received her Ph.D. in Applied Mathematics from MIT in 1999. She received her B.S. in Mathematic from Beijing University in 1994. Her research interest is in measurement error models, dimension reduction, mixed sample problems, latent variable models, selection bias and skew-elliptical distributions, missing not at random problem and more generally semiparametrics. She is currently a fellow of the Institute of Mathematical Statistics and the American Statistical Association.