About the speaker:

Heping Zhang is Susan Dwight Bliss Professor of Biostatistics and Professor of Statistics and Child Study at Yale University. Dr. Zhang published 260 some research articles and monographs in theory and applications of statistical methods and in several areas of biomedical research including epidemiology, genetics, child and women health, mental health, and substance use. He directs the Collaborative Center for Statistics in Science that coordinates major national research networks to understand the etiology of pregnancy outcomes and to evaluate treatment effectiveness for infertility. He is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. He was named the 2008 Myrto Lefokopoulou distinguished lecturer by Harvard School of Public Health and a 2011 Medallion Lecturer by the Institute of Mathematical Statistics. In 2011, he received the Royan International Award on Reproductive Health.   Professor Zhang is the Editor-in-Chief of Statistics and Its Interface, and serves on several editorial boards including the Journal of the American Statistical Association and Genetic Epidemiology.

Abstract：

Residual diagnostics is an important classroom topic in statistics. Nowadays, it only occasionally appears in a statistical journal or research publication, even when regression or association analyses of real data are critical to the findings and conclusions. Perhaps it is a topic we tend to gloss over; or perhaps it is a topic we know neither how to approach nor how to circumvent. The latter is probably closer to reality in the context of logistic regression, and even more so when there is an ordinal-scaled outcome such as whether we feel sad, good or great today. In this talk, I will attempt to draw your attention to this topic, which in my own experience remains very important yet insufficiently treated in real data applications. Using a combination of classic and contemporary statistical techniques, I will introduce the concept of surrogate residuals, which appears informative and useful in residual diagnosis of regression models involving categorical outcomes, including logistic regression. This will be supported in theory, simulation, and real data analysis. The work is a collaboration with Dr. Dungang Liu, Assistant Professor of Business Analytics, Carl H. Lindner College of Business, University of Cincinnati.