Bing Li (academic)

Bing Li is a statistician specializing in sufficient dimension reduction (SDR) and its applications in various fields. He is a Professor of Statistics at Pennsylvania State University.

His research focuses on developing methodologies and theories for reducing the dimensionality of predictors in regression problems without loss of information about the response variable. This involves identifying lower-dimensional subspaces that contain all the relevant information, thereby simplifying the model and improving its interpretability and prediction accuracy.

Li's contributions to SDR include the development and refinement of methods like sliced inverse regression (SIR), sliced average variance estimation (SAVE), and directional regression. He has also worked on extending SDR techniques to accommodate various data types and model structures, such as time series data, functional data, and generalized linear models.

His work has found applications in diverse areas including econometrics, genetics, image analysis, and environmental science. He is also involved in developing statistical methods for analyzing high-dimensional data and addressing challenges related to model selection and inference.

Li received his Ph.D. in Statistics from Rutgers University. He is a Fellow of the Institute of Mathematical Statistics (IMS) and a Fellow of the American Statistical Association (ASA). He has published extensively in leading statistical journals and has presented his research at numerous international conferences.