Nan Lin earned a B.S. (1999) from University of Science and Technology of China, a M.S. (2000) and Ph.D. (2003) in Statistics, and a second M.S. (2003) in Finance from University of Illinois at Urbana-Champaign. Before joining Washington University, he was a postdoctoral associate (2003-2004) at the Center for Statistical Genomics and Proteomics, Yale University.
Nan Lin is an Associate Professor in the Department of Mathematics at Washington University in St. Louis and has a joint appointment in the Division of Biostatistics, Washington University in St. Louis, School of Medicine. His methodological research is in the areas of statistical computing for massive data, Bayesian regularization, bioinformatics and longitudinal and functional data analysis. His applied research involves statistical analysis of data from anesthesiology and genomics. He teaches a wide range of statistics courses, including mathematical statistics, Bayesian statistics, linear models, experimental design, statistical computation, and nonparametric statistics. He is currently an Associate Editor of the journal Computational Statistics & Data Analysis.
1. Xi, R. and Lin, N. (in press), Direct regression modeling of high-order moments in big data, Statistics and Its Interface.
2. Zhang, B.*, Zhou, Y.*, Lin, N.*, Lowdon, R. F.*, Hong, C., Nagarajan, R. P., Cheng, J. B., Li, D., Stevens, M., Lee, H. J., Xing, X., Zhou, J., Sundaram, V., Gu, J., Gascard, P., Sigaroundinia, M., Tisty, T. D., Kadlecek, T., Weiss, A., O’Green, H., Farnham, P. J., Marie, C. L., Ligon, K. L., Madden, P. A. F., Tam, A., Moore, R., Hirst, M., Marra, M. A., Zhang, B., Castello, J. and Wang, T. (2013), Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm, Genome Research, 23, 1522-1540. (*: co-first author)
3. Wang, G., Lin, N. and Zhang, B. (2013), Functional contour regression, Journal of Multivariate Analysis, 116, 1-13.
4. Wang, G., Lin, N. and Zhang, B. (2013), Dimension reduction in functional regression using mixed data canonical correlation analysis, Statistics and Its Interface, 6, 187-196.
5. Xu, L., Lin, N., Zhang, B. and Shi, N. (2012), A finite mixture model for working correlation matrices in generalized estimating equations, Statistica Sinica, 22(2), 755-776.
6. Avidan, M.S., Jacobsohn, E., Glick, D., Burnside, B., Zhang, L., Villafranca, A., Karl, L., Kamal, S., Torres, B., O'Conner, M., Evers, A. S., Gradwohl, S., Lin, N., Palanca, B. J. and Mashour, G. A. (2011), Prevention of intraoperative awareness in a high-risk surgical population, The New England Journal of Medicine, 365, 591-600.
7. Lin, N. and Xi, R. (2011), Aggregated estimating equation estimation, Statistics and Its Interface, 4, 73-84.
8. Li, Q., Xi, R. and Lin, N. (2010), Bayesian regularized quantile regression, Bayesian Analysis, 5,533-556.
9. Li, Q. and Lin, N. (2010), The Bayesian elastic net, Bayesian Analysis, 5,151-170.
10. Lin, N. and Xi, R. (2010), Fast surrogates of U-statistics, Computational Statistics and Data Analysis, 54, 16-24.
11. Xi, R., Lin, N. and Chen, Y. (2009), Compression and aggregation for logistic regression analysis in data cubes, IEEE Transactions on Knowledge and Data Engineering, 21(4), 479-492.
12. Woods, C. and Lin, N. (2009), Item response theory with estimation of the latent density using Davidian curves, Applied Psychological Measurement, 33,102-117.
13. Lin, N. and He, X. (2006), Robust and efficient estimation under data grouping, Biometrika, 93 (1), 99-112.
14. Lin, N. and Zhao, H. (2005), Are scale-free networks robust to measurement errors? BMC Bioinformatics, 6: 119.
15. Lin, N., Wu, B., Jansen, R., Gerstein, M. and Zhao, H. (2004), Information assessment on predicting protein-protein interactions, BMC Bioinformatics, 5: 154.