After one year’s construction, the Academy of Statistics and Interdisciplinary Sciences (ASIS) has progressed rapidly with excellent performances. Up to the end of 2019, one year after the establishment of ASIS, we have recruited 6 faculties all over the world. Besides, two assistant professors will join the ASIS in 2020. All of them are tenure track faculties. Although currently we have few number of faculties, the research levels of our academy are high. Our faculties include one member of the statistical discipline evaluation group of the State Council academic degree committee in China, one recipient of the national outstanding youth fund, one Changjiang distinguished professor, one selected candidate for the hundred talents program of the Chinese Academy of Sciences, one expert with special State Council government allowances, one national candidate for hundreds of millions of talents in the new century of China, two recipients of Shanghai Pujiang talented program. The six faculties, including one professor, one associate professor, four assistant professors, all have experiences studying abroad.
With the guidance of the dean and the excellent academic environment in ASIS, all the young faculties grow quickly and have productive achievements. For example, the assistant professor Huijuan Ma has one paper accepted by the top statistical journal Biometrika, and has published four papers in other statistical journals. She also has two grants, one is from the National Natural Science Foundation of China, one is Shanghai Pujiang program. The young faculties in ASIS and Faculty of Economics and Management (FEM) frequently participate seminars involving interdisciplinary sciences, which are beneficial for their research and teaching.
Our academy will continue recruit excellent faculties at every level, including fresh PhDs, junior and senior researchers. Relevant disciplines include statistics, economics, economic statistics, management sciences, and other interdisciplinary sciences. This will further improve the research abilities as well as the international reputation of our academy.
We also have important progresses and notable achievements in building academic teams. We already developed three academic teams. The first team mainly studies economic statistics and financial econometrics, including Yong Zhou, Yun Shi, Binglin Gong, Lyu Chen, Yingying Zhang, Rui Cui, and Linyi Qian as members. The second team mainly studies big data and complicated data analysis, the members include Yong Zhou, Huijuan Ma, Zhou Yu, Yukun Liu, Dongdong Xiang, and Miaomiao Yu. The third term mainly studies management sciences and business intelligence, including Yong Zhou, Luyao Zhang, Gang Du, Wei Xie, Min Zhang, Xiaolan Zhou, and Qian Qian as members. At the first half year of 2019, our academy only had three faculties, their programs were all granted by National Natural Science Foundation of China. The key program, whose participants were mainly from the big data and complicated data analysis academic team, was one of the three granted key programs in ECNU at 2019. Besides, Huijuan Ma was granted by Shanghai Pujiang program. Currently, we have 5 programs granted, including 2 key state programs of National Natural Science Foundation of China.
No. | Approval NO. | PI | Name | Category |
1 | 71931004 | Zhou Yong | Methods of complicated data and behavior analysis and their applications in economics and management | State Key Program of NSFC |
2 | 71971083 | Shi Yun | Portfolio selection and asset pricing with probability weighting | NSFC |
3 | 11901200 | Ma Huijuan | Semiparametric regression for multivariate recurrent event data with missing event type | NSFC |
4 | 19PJ1403400 | Ma Huijuan | Quantile regression of latent longitudinal trajectory features | Shanghai Pujiang Program |
5 | 91546202 | Zhou Yong | Statistical inference for financial big data with its applications | State Key Program of NSFC |
Besides, the academic influence of ASIS becomes bigger. We have successfully recruited two postdocs. This is competitive because a large number of related working opportunities exist.
We are focusing on the research frontier and development trend of statistics, concentrated 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 solving some urgent strategic problems. For the interdisciplinary research between statistics and economics, we will put efforts on developing econometrics, financial risk management for internet 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 etc. For the interdisciplinary research between statistics and public administration, we will focus on studying management of public health, management of precision medicine etc. In addition, our academy will include research on educational statistics, sport statistics, management statistics, biostatistics, psychometrics, along with research in other interdisciplinary fields.
Up to the beginning of 2020, all the faculties in our academy have published or accepted 37 papers after joining ECNU. A total of 31 papers are SCI or SSCI, including one paper accepted by the statistical top journal “Biometrika” and one paper accepted by the top econometrics journal “Journal of Econometrics”. Yong Zhou, Huijuan Ma, Yun Shi, Lyu Chen, and Yingying Zhang respectively have 30, 5, 2, 1 and 1 papers.
The Academy is focusing on the field of statistics and dedicated to develop frontier theories and methods in statistics. In the meantime, the Academy aims to promote the cross-integration and development of other disciplines such as economics, business administration and public administration. We are expected that our Academy of Statistics and Interdisciplinary Sciences will be national leader for statistical interdisciplinary research. Further, the Academy will be the important and high level base for training and exchanging scholars with significant international influence. Taking the statistics of the world’s leading universities as the benchmark, we will create a first-class innovation team. The Academy uses the Academy of Advanced Studies at Princeton University as a model to conduct high-end scientific research in statistics and related interdisciplinary studies, and to advance the innovation and development for statistics and its interdisciplinary areas.
Welcome excellent scholars all over the world to join the Academy of Statistics and Interdisciplinary Sciences. Guiding by the development strategies of ECNU, hope we will have more achievements.
Published or accepted peer-reviewed papers from May 2018 to March 2020
1. 1. Song, X.Y., Kim D., Yuan, H.L., Cui, X.Y. Lu, Z.P. Zhou, Y. and Wang, Y. (2020) Volatility analysis with realized GARCH-Ito models.Journal of Econometrics, accepted. (计量经济学TOP，经管A)
2. Chen, L.J. and Zhou, Y.* (2020). Quantile regression in big data: a divide and conquer based strategy. Computational Statistics & Data Analysis.(SCI二区)
3. Xun, L. *, Tao, Li. and Zhou, Y. (2020). Estimators of quantile difference between two samples with length-biased and right-censored data. Test, https://doi.org/10.1007/s11749-019-00657-3.
4. Ma, H.J., Zhao, W. and Zhou, Y.*(2020). Semiparametric model of mean residual life with biased sampling data. Computational Statistics & Data Analysis, doi: https://doi.org/10.1016/j.csda.2019.106826. (SCI二区)
5. Yuan, H. L., Mu, Y. * and Zhou, Y. (2020). Leverage Effect in High-Frequency Data with Market Microstructure. Statistics and Its Interface.13(1):91-101.
6. Ma, H., Peng, L., Huang, C-Y., and Fu, H. (2020). Heterogeneous individual risk modelling with recurrent events. Biometrika. Accepted.（统计学TOP 经管A）
7. Yingying Zhang, Heng Lian, Guodong Li and Zhongyi Zhu(2020). Functional additive quantile regression. Statistica Sinica. Accepted.（经管A-）
8. Liu,X.Q.*, Song, X. Y. and Zhou, Y. (2019.12). Likelihood ratio-type tests in weighted composite quantile regression of DTARCH models. Science China-Mathematics , 62(12), 2571-2590. (SCI一区)
9. Liu, Y., Zhang, S.* and Zhou, Y. (2019.9). Semiparametric quantile-difference estimation for length-biased and right-censored data. Science China-Mathematics, 62(9), 1823-1838.(SCI一区)
10.Song, SS., Zhou, Y.*(2019.7): Nonparametric estimation of the ROC curve for length-biased and right-censored data, Communications in Statistics-Theory and Methods, DOI: 10.1080/03610926.2019.1604963
11.Xu, D *, and Zhou, Y.(2019.9.22). Local Composite Partial Likelihood Estimation for Length-Biased and Right-Censored Data. Journal of Statistical Computation and Simulation, 89(14), 2661–2677.
12.Chen, XR. *, Chen, Y., Wan, ATK and Zhou, Y. (2019.6). On the asymptotic non-equivalence of efficient-GMM and MEL estimators in models with missing data. Scandinavian Journal of Statistics, 46(2), 361-388.DOI: 10.1111/sjos.12354.
13.Li, Y.M.*, Zhou, Y. (2019.3). The Kaplan-Meier estimator and hazard estimator for censored END survival time observations. Communications in Statistics-Theory and Methods, DOI: 10.1080/03610926.2019.1580737.
14. Xun, L., Zhou, YZ., Zhou, Y.*(2019.3).A Generalization of Expected Shortfall Based Capital Allocation.Statistics & Probability Letters, 146, 193–199. DOI: 10.1016/j.spl.2018.10.014
15.Pan, J.*, Zhang, S. C. and Zhou, Y. (2019.2.11). Variable screening for ultrahigh dimensional censored quantile regression.Journal of Statistical Computation and Simulation, 89(3), 395-413.
16.Ma, H.J., Shi, J.H.* and Zhou, Y. (2019). Proportional mean residual life model with censored survival data under case-cohort design. Statistics and Its Interface. 12(1),21-33.
17.Liu, Y.T., Lin, C.J. * andZhou, Y. (2019). Nonparametric estimate of conditional quantile residual lifetime for right censored data. Statistics and Its Interface,12(1): 61-70. DOI: 10.4310/SII.2019.v12.n1.a6
18.Zhang, F.P., Peng, H.* and Zhou, Y. (2019). Fine-Gray Proportional subdistribution hazards model for competing risks data under length-biased sampling. Statistics and Its Interface, 12(1): 107-122. DOI: 10.4310/SII.2019.v12.n1.a10
19.Li, C.B.*and Zhou, Y. (2019.1.7). The Estimation for the General Additive-Multiplicative Model Using the Length-Biased Survival Data. Statistical Papers, Published online. https://doi.org/10.1007/s00362-018-01079-3
20.X. Y. Cui, J. J. Gao, Y. Shi*, and S.S. Zhu, 2019, Time-Consistent Strategy and Self-Coordination Strategy for Multi-period Mean- Conditional Value-at-Risk Portfolio Selection, European Journal of Operational Research, 276(2), 781-789.（SCI二区，管理科 学TOP）.
21.X. Y. Cui, J. J. Gao, and Y. Shi*, 2019, Multi‑period mean-variance portfolio optimization with management fees, OperationalResearch, forthcoming. https://doi.org/10.1007/s12351-019-00482-4
22. Ma, H., Peng, L. and Fu, H. (2019). Quantile regression modeling of latent trajectory features with longitudinal data. Journal of Applied Statistics. 46(16), 2884-2904.
23.Lv Chen and Yang Shen* (2019). Stochastic Stackelberg differential reinsurance games under time-inconsistent mean–variance framework. Insurance: Mathematics and Economics,88, 120-137. [SSCI].（经管A-）
24.Zhang, S.C., Pan, J.* and Zhou, Y. (2018.12). Robust Conditional Nonparametric Independence Screening for Ultrahigh-Dimensional Data. Statistics & Probability Letters, 143: 95-101.
25.Pan, J.*, Yu, Y., and Zhou, Y. (2018.10). Nonparametric independence feature screening for ultrahigh-dimensional survival data.Metrika, 81(7): 821–847.
26.Zhang, F.P.*, Zhao. X.Q. and Zhou, Y. (2018.8). An embedded estimating equation for the additive risk model with biased-sampling data.Science China-Mathematics, 61(8): 1495-1518. (SCI一区)
27.Yu, Y., He, D., and Zhou, Y.* (2018). Robust model-free feature screening based on modified Hoeffding measure for ultra-high dimensional data. Statistics and Its Interface, 11(3): 473-489.
28.Wang, X. J.*, Zhou, Y. and Liu, Y. (2018). Semiparametric varying-coefficient partially linear models with auxiliary covariates.Statistics and Its Interface, 11(4): 587-602.
29.Fan, C., Ma, H.* and Zhou, Y. (2018). Quantile regression for competing risks analysis under case-cohort design. Journal of Statistical Computation and Simulation. 88: 1060-1080.
30.苑慧玲,徐路,周勇*.带有市场交易信息和随机微观噪声下的杠杆效应研究[J/OL].中国管理科学:1-12[2020-02-26].https://doi.org/10.16381/j.cnki.issn1003-207x.2018.1363.(基金委权威A)
31.管欣,周勇.风险度量ES半参数模型估计及其应用[J].应用数学学报,2019,42(06):744-760.
32.马秋霞,潘生,周勇*.长度偏差右删失数据下均值剩余寿命的复合估计[J].中国科学:数学,2019,49(05):781-798.（经管B）
33.荀立,周杨志,周勇.Haezendonck-Goovaerts资金分配的估计[J].数理统计与管理,2019,38(05):919-928.（权威B）
34.徐达,周勇*.病例-队列设计下长度偏差数据的比例均值剩余寿命模型的统计推断[J].应用数学学报,2019,42(03):318-333.
35.何迪,周勇*.基于状态空间模型的宏观经济因素对股市流动性的建模分析[J].中国管理科学,2019,27(05):42-49.（基金委权威A）
36.徐达,周勇.基于广义病例-队列设计方案的长度偏差数据回归分析[J].吉林大学学报(理学版),2019,57(02):311-316.
37.李永明*,周勇.基于右删失宽相依数据的Kaplan-Meier估计和风险率估计的渐近性质[J].应用数学学报,2019,42(01):71-84.
