Areas of Focus
Sequential Selection for Accelerated Life Testing via Approximate Bayesian Inference
Approximate Bayesian inference (Chen and Ryzhov, 2019) has been proposed to construct computationally tractable statistical learning procedures for incomplete or censored data. In this talk, I will discuss a sequential model-updating procedure via approximate Bayesian inference for the Log-normal model with censored observations. We show that the proposed procedure leads to a consistent model parameter estimation. The developed model updating procedure also enables a closed form expression of a sequential design criterion. The proposed procedure is applied to accelerated life testing experiments, which aims at determining the material alternative with the best reliability performance.
Dr. Qiong Zhang is an assistant professor in the School of Mathematical and Statistical Sciences at Clemson University. Previously, she was an assistant professor of statistics at Virginia Commonwealth University in 2014–2018. Dr. Zhang received a B.S. degree in statistics from Nankai University and an M.S. degree in statistics from Peking University in 2007 and 2009, respectively. She received her Ph.D. degree in statistics from University of Wisconsin-Madison in 2014. Dr. Zhang’s research interests include the interface between information collection and statistical modeling, design and analysis of computer experiment, and uncertainty quantification.