“中国人民大学国际统计论坛”创办于2004年，致力于搭建统计学界高层次的学术交流平台，已成为中国最有影响力的统计学论坛之一。

2023年7月14日至15日，本届论坛将邀请5位主题报告人和6位特邀报告人，本次介绍特邀报告人Cunhui Zhang，预祝第九届中国人民大学国际统计论坛取得圆满成功！

Cunhui Zhang

Title

Adaptive Inference in Sequential Experiments

Abstract

Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes. Despite its advantages, such data collection mechanism often introduces complexities to the statistical inference procedure. For instance, the ordinary least squares estimator in an adaptive linear regression model can exhibit non-normal asymptotic behavior, posing challenges for accurate inference and interpretation. We propose a general method for constructing debiased estimator which remedies this issue. The idea is to make use of adaptive linear estimating equations. We establish theoretical guarantees of asymptotic normality, supplemented by discussions on achieving near-optimal asymptotic variance. A salient feature of our estimator is that in the context of multi-armed bandits, our estimator retains the non-asymptotic performance of the least square estimator while obtaining asymptotic normality property. Consequently, this work helps connect two fruitful paradigms of adaptive inference: a) non-asymptotic inference using concentration inequalities and b) asymptotic inference via asymptotic normality.

Biography

Cun-Hui Zhang, Distinguished Professor of Statistics at Rutgers University, USA, is an elected fellow of the Institute of Mathematical Statistics and American Statistical Association. His research interests include high-dimensional data, empirical Bayes, nonparametric and semiparametric inference, among other topics. In particular, he is known for his work on regularized estimation with the minimax concave penalty, de-biased inference with high-dimensional data, deconvolution and general maximum likelihood empirical Bayes, tensor completion and tensor time series, L-1 data depth, and doubly censored and length-biased data.