|Office:||210B Cullimore Hall|
|Hours:||M 1:30 pm - 3:30 pm (Spring 2018)|
I am presently an Associate Professor of Statistics in the Department of Mathematical Sciences at the New Jersey Institute of Technology. Before coming to New Jersey, I worked as a Research Fellow at the National Institute of Environmental Health Sciences for two years. I received my Ph.D. in Biostatistics from the Department of Environmental Health at the University of Cincinnati in 2007.
My research interests include: Large-scale multiple testing, Selective Inference, High-dimensional data analysis, Bioinformatics, Machine learning, Biostatistics, Adaptive design, Statistical methods for clinical trials.
- Math 654: Design and Analysis of Clinical Trials
- Math 644: Regression Analysis Method
- Math 448: Stochastic Simulation
- Math 447: Applied Time Series Analysis
- Math 344: Regression Analysis
- Math 664: Methods for Statistical Consulting
- Math 660: Introduction to Statistical Computing
- Math 659: Survival Analysis
- Sarkar, S., Fu, Y. and Guo, W. (2016). Improving Holm's procedure using pairwise dependencies. Biometrika, 103, 237-243.
- Grandhi, A., Guo, W. and Peddada, S. (2016). A multiple pairwise comparisons reveals differential gene expression patterns and pathways in uterine fibroids by their size. BMC Bioinformatics, 17:104.
- Guo, W. and Romano, J. P. (2015). On stepwise control of directional errors under independence and some dependence. Journal of Statistical Planning and Inference, 163, 21-33.
- Qiu, Z., Guo, W. and Lynch, G. (2015). On generalized fixed sequence procedures for controlling the FWER. Statistics in Medicine, 34, 3968-3983.
- Guo, W., Li He and Sarkar, S. (2014). Further results on controlling the false discovery proportion. Annals of Statistics, 42, 1070-1101.
- Sarkar, S., Chen, J. and Guo, W. (2013). Multiple testing in a two-stage adaptive design with combination tests controlling FDR. Journal of the American Statistical Association, 108, 1385-1401.
- Guo, W., Yang, M., Xing, C. and Peddada, S. (2012). Analysis of high dimensional data using pre-defined set and subset information, with applications to genomic data. BMC Bioinformatics, 13:177.
- Sarkar, S., Guo, W. and Finner, H. (2012). On adaptive procedures controlling the familywise error rate. Journal of Statistical Planning and Inference, 142, 65-78.
- Guo, W. and Rao, M. B. (2010). On stepwise control of the generalized familywise error rates. Electronic Journal of Statistics, 4, 472-485.
- Sarkar, S. and Guo, W. (2010). Procedures controlling generalized false discovery rate using bivariate distributions of the null p-values. Statistica Sinica, 20, 1227-1238.
- Guo, W., Sarkar, S. and Peddada, S. (2010). Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories. Biometrics, 66, 485-492.
- Guo, W. (2009). A note on adaptive Bonferroni and Holm's procedures under dependence. Biometrika, 96, 1012-1018.
- Sarkar, S. and Guo, W. (2009). On a generalized false discovery rate. Annals of Statistics, 37, 1545-1565.
- Guo, W. and Peddada, S. (2008). Adaptive choice of the number of bootstrap samples in large scale multiple testing. Statistical Applications in Genetics and Molecular Biology, 7(1), Article 13.
- Guo, W. and Rao, M. B. (2008). On optimality of the Benjamini-Hochberg procedure for the false discovery rate. Statistics and Probability Letters, 78, 2024-2030.
- Guo, W. and Rao, M. B. (2008). On control of the false discovery rate under no assumption of dependency. Journal of Statistical Planning and Inference, 28, 3176-3188.
- Guo, W. and Romano, J. (2007). A generalized Sidak-Holm procedure and control of generalized error rates under independence. Statistical Applications in Genetics and Molecular Biology, 6(1), Article 3.
For a complete list of publications, see my research website: https://web.njit.edu/~wguo/research.html