We focus on the research frontier and development trend of statistics, concentrate on the significant demand of development strategy in China and Shanghai, study on forward-looking problems in some cutting-edge fields of science and technology, and solve some urgent strategic problems.

For the interdisciplinary research between statistics and economics are interdisciplinary, we will put efforts on developing econometrics, financial risk management for internet and etc. For the interdisciplinary research between statistics and business administration, we will study metrological methods for electronic business, decision-making behavior for subpopulation and individuals under big data and etc. For the interdisciplinary research between statistics and public administration, we will focus on study management of public health, management of precision medicine and etc. In addition, our academy will include research on educational statistics, sport statistics, management statistics, biostatistics, psychometrics, along with research in other interdisciplinary fields.

Representative papers in recent five years：

1.Chen, X., Chen, Y., Wan, A. and Zhou, Y. (2019). On the asymptotic non-equivalence of efficient-GMM and MEL estimators in models with missing data. Scandinavian Journal of Statistics, 46, 361-388.

2.He, D., Zhou, Y. and Zou, H. (2019). High-dimensional variable selection with right censored length-biased data. Statistica Sinica, DOI: 10.5705/ss.202018.0089.

3.Fan, C. Lu, W., Song, R. and Zhou, Y. (2017). Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes. Journal of the Royal Statistical Society, Series B, 79, 1565-1582.

4.Bai, F., Huang, J. and Zhou, Y. (2016).  Semiparametric inference of mean residual life model with right censored and length-biased data. Statistica Sinica, 26, 1129-1158

5.Chen, X., Wan, A. and Zhou, Y. (2015). Efficient quantile regression analysis with missing observations. Journal of the American Statistical Association, 110, 723-741.

6.Liu, X., Jiang, H. and Zhou, Y. (2014). Local empirical likelihood inference for varying-coefficient density-ratio models based on case-control data. Journal of the American Statistical Association, 107, 635-646

7.Ma, H., Peng, L., Zhang, Z. and Lai, H-C. (2018). Generalized accelerated recurrence time models for multivariate recurrent events data with missing event type. Biometrics, 74, 954-965.

8.Shi, Y., Cui, X., Yao, J. and Li, D. (2015). Dynamic trading with reference point adaptation and loss aversion. Operations Research, 63, 789-806.

Ongoing research projects:

1. Major Program of National Natural Science Foundation of China “Statistical learning theory and methods for financial big data, with applications to internet of finance”(No. 91546202), 2016-2020, Principal Investigator, Yong Zhou, RMB 2.4 million.

2. National Natural Science Foundation of China “A New Framework for Time Inconsistency Issue based on Goal-setting and Self-control and its Applications”(No. 71601107), 2017-2019, Principal Investigator, Yun Shi, RMB 180000.