A Panel Count Model with Time-varying Coefficients and a Semiparametric Model with Image


Zhangsheng Yu, Department of Bioinformatics & Biostatistics, Department of Statistics, Shanghai Jiao Tong University


2019.01.15 14:00-15:00


520 Pao Yue-Kong Library


In this talk, I will first present a nonparametric time-varying coefficient model for the analysis of panel count data. We extend the traditional panel count data models by incorporating B-splines estimates of time-varying coefficients. We show that the proposed model can be implemented using a nonparametric maximum pseudo-likelihood method. We further examine the theoretical properties of the estimators of model parameters. The operational characteristics of the proposed method are evaluated through a simulation study. For illustration, we analyze data from a study of childhood wheezing, and describe the time-varying effect of an inflammatory marker on the risk of wheezing. I will also present some works on linear regression model with parametric coefficient and unstructured image as the input for prediction.


Dr. Yu’s research interests include clinical statistics methods and the collaborative research in health science. He has developed advanced survival methodologies for disease risk analysis. His current research focus on panel count model, cure rate model, and regression models incorporating image using deep neural network. He collaborates with medical investigators in the areas of liver, kidney diseases, anesthesiology etc. He serves as the Associate Editor or Editorial Board Member of multiple journals (e.g. Statistics in Medicine, Heart Rhythm, Journal of Digestive Disease). He served as the President of the Central Indiana Chapter of American Statistical Association and currently serves as the Vice-president of Clinical Statistics Chapter of World Congress of Chinese Medicine.