Bin Nan, Ph.D.

Bin Nan

Professor of Biostatistics

Professor of Statistics

M4055 SPH II      Vcard icon
1415 Washington Heights
Ann Arbor, Michigan 48109-2029

Office: (734) 763-5538; Fax: (734) 763-2215


Website(s): Personal Website

Curriculum Vitae (PDF)

Professional Summary

Bin Nan is Professor of Biostatistics and Statistics at the University of Michigan. He received his Ph.D. in biostatistics from the University of Washington in 2001 and joined the faculty at the University of Michigan in the same year. Prior to his graduate study in the United States, he had been teaching and doing research in statistical quality control and operations research for the aerospace industry in China. Dr. Nan's research interests are in various areas of statistics and biostatistics including semiparametric inference, failure time and survival analysis, longitudinal data, missing data and two-phase sampling designs, and high-dimensional data analysis. He is also collaborating in many studies in areas of epidemiology, bioinformatics, and brain imaging, particularly in cancer, HIV, women's health, and neurodegenerative diseases .

Courses Taught

BIOSTAT602: Biostatistical Inference


Ph.D., Biostatistics, University of Washington, 2001
M.S., Biostatistics, University of Washington, 1999
M.S., Statistics, Virginia Commonwealth University, 1997
M.S., Aerospace Engineering, Beijing University of Aeronautics& Astronautics, 1987
B.S., Aerospace Engineering, Beijing University of Aeronautics& Astronautics, 1984

Research Interests & Projects

I am interested in all statistical problems arising from my collaboration in biomedical research. I am currently focusing on the development of new methods in the areas of survival analysis, analysis of high-dimensional brain image data, and analysis of longitudinal data with change-points, terminal events, and variables subject to limit of detection.

Selected Publications

Search PubMed for publications by Bin Nan >>

Wang X, Nan B, Zhu J, Koeppe R (2014). Regularized 3D Functional Regression for Brain Image Data via Haar Wavelets. Annals of Applied Statistics, 8, 1045-1064.

Kong S, Nan B (2014). Non-asymptotic oracle inequalities for the high-dimensional Cox regression via lasso. Statistica Sinica, 24, 25-42.

Nan B, Wellner JA (2013). A general semiparametric Z-estimation approach for case-cohort studies. Statistica Sinica, 23, 1155-1180.

Ding Y and Nan B (2011). A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data. Annals of Statistics, 39, 3032-3061.

Hu T, Nan B, Lin X, Robins J (2011). Time dependent cross-ratio estimation for bivariate failure times. Biometrika, 92, 341-354.

Nan, B., Kalbfleisch, J.D., and Yu, M. (2009). Asymptotic theory for the semiparametric accelerated failure time model with missing data. Annals of Statistics, 37, 2351-2376.

Li, Z., Gilbert, P., and Nan, B. (2008). Weighted likelihood method for grouped survival data in case-cohort studies with application to HIV vaccine trials. Biometrics, 64, 1247-1255.

Yu, M. and Nan, B. (2006). A revisit of semiparametric regression models with missing data. Statistica Sinica, 16, 1193-1212.

Nan, B., Lin, X., Lisabeth, L. D., and Harlow ,S. D. (2006). Piecewise constant cross-ratio estimation for association of age at a marker event and age at menopause. Journal of the American Statistical Association, 101, 65-77.

Nan, B., Emond, M., and Wellner, J.A. (2004). Information bounds for Cox regression models with missing data. Annals of Statistics, 32, 723-753.

Professional Affiliations

American Statistical Association, Fellow
Institute of Mathematical Statistics, Fellow
International Biometric Society - ENAR
International Chinese Statistical Association
International Statistical Institute, Elected Member
Organization for Human Brain Mapping