讲座题目：Statistical methods for degradation data with dynamic covariates and an Application to Weathering Data

主讲人：洪益力教授 弗吉尼亚理工大学

讲座时间：2019年6月11日（星期二）15:00-16:00

讲座地点：综合楼601

主讲人简介：Yili Hong received a BS in statistics in 2004 from University of Science and Technology of China. He received his MS in statistics in 2005 and PhD in statistics in 2009 from Iowa State University. He is currently an Associate Professor in the Department of Statistics at Virginia Tech. His research mainly focuses on statistical reliability. Areas include lifetime data analysis, field failure prediction, accelerated life test planning and analysis, accelerated degradation test planning and data analysis, system health monitoring, and applications in engineering, chemistry and material sciences. His research has been published in top journals such as Technometrics, JQT, Annals of Applied Statistics, JASA, IEEE Transactions on Reliability, and Quality Engineering. He is one of the recipients of the 2011 DuPont Young Professor Award. He is an associate editor for Technometrics and JQT. He is a co-guest editor for a special issue on big data in reliability for JQT. He is an elected member of International Statistical Institute.  

讲座摘要：Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this paper, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity).