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 .
- Biostat 801: Advanced Inference I
- 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.
- Kong S, Nan B. Semiparametric approach to regression with the covariate subject to a detection limit. Biometrika (in press).
- Das R, Banerjee M, Nan B, Zheng H. Fast estimation of regression parameters in a broken-stick model for longitudinal data. Journal of the American Statistical Association (in press).
- Shu H, Nan B, Koeppe R (2015). Multiple testing for neuroimaging via hidden Markov random field. Biometrics 71, 741-750.
- Li Y, Nan B, Zhu J (2015). Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure. Biometrics 71, 354-363.
- 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.
- Foster J, Taylor J, Nan B (2013). Variable selection in monotone single-index models via the adaptive lasso. Statistics in Medicine 32, 3944-3954.
- Nan B, Wellner JA (2013). A general semiparametric Z-estimation approach for case cohort studies. Statistica Sinica 23, 1155-1180.
- Li Z and Nan B (2011). Relative risk regression for current status data in case-cohort studies. Canadian Journal of Statistics 39, 557-577.
- 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.
- 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