01/30/2023
The ASA Philadelphia chapter is delighted to announce a webinar on Deep Neural Networks Guided Ensemble Learning for Point Estimation.
The webinar will be on Thursday, February 16 from 11:00am to 12:00pm Eastern Time. The talk will be given by Dr. Tianyu Zhan, Senior Manager of Data and Statistical Sciences at AbbVie.
Register for free using the following link. After registering, you will receive a confirmation email containing information about joining the meeting.
https://drexel.zoom.us/meeting/register/tZ0uf-CtrjwsGdwthvU5EwWHlfhXshXJWmHe
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.
Look forward to seeing you at the webinar!
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 pro...
