February 16, 2023 Webinar
Deep Neiral Networks Guided Ensemble Learning for Point Estimation
Tianyu Zhan, PhD
Abstract
In modern statistics, interests shift from pursuing the uniformly minimum variance unbiased estimator to reducing mean squared error (MSE) or residual squared error. However, the characterization of such optimal statistics in terms of minimizing MSE remains open and challenging in many problems, for example estimating treatment effect in adaptive clinical trials with pre-planned modifications to design aspects based on accumulated data. From an alternative perspective, we propose a deep neural network based automatic method to construct an improved estimator from existing ones. Simulation studies demonstrate that the proposed method has considerable finite-sample efficiency gain as compared with several common estimators. In the Adaptive COVID-19 Treatment Trial (ACTT) as an important application, our ensemble estimator essentially contributes to a more ethical and efficient adaptive clinical trial with fewer patients enrolled.
Short Bio
Tianyu is a Senior Manager of Data and Statistical Sciences at AbbVie. His research interests include adaptive clinical trials, Bayesian analysis, machine learning, missing data, multiplicity control, and survival analysis. He is an active journal referee and an AE of Journal of Biopharmaceutical Statistics. Tianyu received his Ph.D. in Biostatistics from the University of Michigan, Ann Arbor in 2017.