Email: weiqian@udel.edu
Department of Applied Economics and Statistics
University of Delaware
I am an Associate Professor of Statistics at the University of Delaware. My research interests include high-dimensional statistics, sequential decision making, model selection, dimension reduction, nonparametric and semiparametric estimation, statistical computing, lifetime data analysis, actuarial statistics, forecasting, network modeling, deep learning, reinforcement learning, and data science applications. I am particularly interested in investigating statistical machine learning methods for analysis of complex and big data stemming from applied problems.
EDUCATION
Ph.D. in Statistics, minor in Mathematics, University of Minnesota, Twin Cities, July 2014. Advisor: Yuhong Yang
M.S. in Chemistry, Stanford University, July 2008.
B.S. in Chemistry, Peking University, Beijing, July 2006.
ACADEMIC AFFILIATION
Associate Professor, Department of Applied Economics and Statistics, University of Delaware, 2021 -- present.
JPMC Fellow, Institute for Financial Services Analytics, University of Delaware, 2017 -- present.
Affiliated Faculty, Data Science Institute, University of Delaware.
Assistant Professor, Department of Applied Economics and Statistics, University of Delaware, 2017 -- 2021,
Assistant Professor, School of Mathematical Sciences, Rochester Institute of Technology, 2014 -- 2017.
PUBLICATIONS
Qian, W., Ing, C.-K. and Liu, J. (2022+) An Adaptive Algorithm to Multi-armed Bandit Problem with High-dimensional Covariates. Journal of the American Statistical Association, in press.
Laux, P., Qian, W. and Zhang, H. (2023) Learning from Lending in the Interbank Network. Data Science in Science, 2, 1-19.
Zhou, H., Qian, W. and Yang, Y. (2022) Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data. Communications in Statistics - Simulation and Computation, 51, 5507-5529.
Qian, W., Rolling, C. A., Gang C. and Yang, Y. (2021) Combining Forecasts for Universally Optimal Performance. International Journal of Forecasting, 38, 193-208.
Zhang, H., Zhao, X., Yin, K., Yan, Y., Qian, W., Chen, B. and Fang, X. (2021) Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread. Journal of Public Health Research, 10, 1906.
Ding, S., Qian, W. and Wang, L. (2020) Double-Slicing Assisted Sufficient Dimension Reduction for High Dimensional Censored Data. The Annals of Statistics, 48, 2132-2154.
Fontaine, S., Yang, Y., Qian, W., Gu, Y. and Fan, B. (2020) A Unified Approach to Sparse Tweedie Modeling of Multi-source Insurance Claim Data, Technometrics, 62, 339-356.
Qian, W., Ding, S. and Cook, R. D. (2019) Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension, Journal of the American Statistical Association, 114, 1277-1290.
Qian, W., Li, W., Sogawa, Y., Fujimaki, R., Yang, X. and Liu, J. (2019) An Interactive Greedy Approach to Group Sparsity in High Dimensions, Technometrics, 61, 409-421.
Qian, W., Rollling, C. A., Cheng, G. and Yang, Y. (2019) On the Forecast Combination Puzzle, Econometrics, 7, 39.
Yang, Y., Qian, W. and Zou, H. (2018) Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models, Journal of Business and Economic Statistics, 36, 456-470.
Munoz, L. M., Gelzer, A. R., Fenton, F. H., Qian, W., Lin, W., Gilmour, R. F. and Otani, N. F. (2018) Discordant Alternans as a Mechanism for Initiation of Ventricular Fibrillation In Vitro, Journal of the American Heart Association, 7, 1-24.
Agarwal, A., Bajorski, P., Farnsworth, D. L., Marengo, J. E., and Qian, W. (2017) The Conditional Poisson Process and the Erlang and Negative Binomial Distributions, Open Journal of Statistics, 7, 16-22.
Qian, W. and Yang, Y. (2016) Kernel Estimation and Model Combination in a Bandit Problem with Covariates, Journal of Machine Learning Research, 17(149), 1-37.
Qian, W. and Yang, Y. (2016) Randomized Allocation with Arm Elimination in a Bandit Problem with Covariates, Electronic Journal of Statistics, 10, 242-270.
Qian, W., Yang, Y. and Zou, H. (2016) Tweedie's Compound Poisson Model with Grouped Elastic Net, Journal of Computational and Graphical Statistics, 25, 606--625.
Qian, W. and Yang, Y. (2013) Model Selection via Standard Error Adjusted Adaptive Lasso, Annals of the Institute of Statistical Mathematics, 65, 295-318.
Qian, W. and Yang, Y. (2012) Randomized Allocation with Dimension Reduction in a Bandit Problem with Covariates, In Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery, 1537-1541.
Patel, R., Caraviello, D. and Qian, W. (2012) Improving Lasso Performance for Grey Leaf Spot Disease Resistance Prediction Based on Genotypic Data by Considering All Possible Two-Way SNP Interactions, Integrative Biology, 4, 564-567.
Trost, B. M., Sieber, J. D., Qian, W., Dhawan, R. and Ball, Z. T. (2009) Asymmetric Total Synthesis of Soraphen A: a Flexible Alkyne Strategy, Angewandte Chemie, 121, 5586-5589.
FUNDING
The investigator is grateful for current funding support by:
JPMorgan Chase (JPMC) Faculty Fellowship, 2019 -- 2025.
National Science Foundation grant NSF DMS-1916376, 2019 -- 2023.
National Institute of Health grant NIH R21NS122033 (co-I), 2021 -- 2023.
SOFTWARE
MATLAB package IGA: An interactive greedy and stepwise approach to group sparsity learning in high dimensions. Available on GitHub.
R package HDtweedie: The lasso for the Tweedie's compound Poisson model using an IRLS-BMD algorithm. Particularly useful for variable selection and prediction tasks in insurance claim data analysis. Available on CRAN.
R package TDboost: A boosted Tweedie compound Poisson model using gradient boosting. Capable of fitting a flexible nonlinear Tweedie compound Poisson model and capturing interactions among predictors. Available on CRAN.
R package sealasso: Standard error adjusted adaptive lasso. New weight assignment strategy is particularly useful when collinearity of design matrix is a concern. Available on CRAN.
TEACHING
STAT 603 Statistical Computing and Optimization, Spring 2018 -- 2023
STAT 616 Advanced Design and Reinforcement Learning, Fall 2021 -- 2022
STAT 601 Probability Theory for Operation Research and Statistics, Fall 2017 -- 2022
STAT 470 Intro to Stat Analysis I, Fall 2018
Rochester Institute of Technology
STAT 345 Nonparametric Statistics, Fall 2014 -- 2017
MATH 251 Probability and Statistics I, Fall 2014 -- Spring 2017