何道江 - 安徽师范大学 - 数学与统计学院

个人简介

何道江，男，1980年生，安徽六安人，博士，教授，现任安徽师范大学数学与统计学院副院长，主要研究方向是数理统计、应用统计和数学建模。二、主要学习经历： 1998.9－2002.7安徽师范大学本科生，获理学学士学位 2003.9－2006.7安徽师范大学硕士研究生，获理学硕士学位 2008.9－2011.7北京理工大学博士研究生，获理学博士学位 2015.3－2015.7中国科学院数学与系统科学研究院，访问学者 2016.5－2016.8美国密苏里大学，访问学者 2018.7－2018.8新加坡国立大学，访问学者三、讲授课程：本科生：数理统计、时间序列分析、计量经济学、数学建模、生物统计学研究生：高等数理统计、多元统计分析、线性模型、Bayes统计分析

研究领域

数理统计、应用统计和数学建模

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

[1] He Daojiang, Sun Dongchu*, He Lei. Objective Bayesian analysis for the Student-t linear regression model. Bayesian Analysis, 2020+, available online. (SCI) [2] Xu Kai*, He Daojiang. Omnibus model checks of linear assumptions through distance covariance. Statistica Sinica, 2019+, available online. (SCI) [3] He Daojiang*, Tao Mingzhu. Statistical analysis for the doubly accelerated degradation Wiener model: an objective Bayesian approach. Applied Mathematical Modelling, 2020, 77: 378–391. (SCI) [4] Cao Mingxiang, Park Junyong*, He Daojiang. A test for the k sample Behrens-Fisher problem in high dimensional data. Journal of Statistical Planning and Inference, 2019, 201: 86–102. (SCI) [5] He Daojiang*, Wang Yunpeng, Chang Guisong. Objective Bayesian analysis for the accelerated degradation model based on the inverse Gaussian process. Applied Mathematical Modelling, 2018, 61: 341–350. (SCI) [6] Cao Mingxiang*, He Daojiang. Admissibility of linear estimators of the common mean parameter in general linear models under a balanced loss function. Journal of Multivariate Analysis, 2017, 153: 246–254. (SCI) [7] Xu Kai, He Daojiang*. The superiority of Bayes estimators in multivariate linear model with respect to normal–inverse Wishart priors. Acta Mathematica Sinica, English Series, 2015, 31: 1003–1014. (SCI) [8] He Daojiang*, Wu Jie. Admissible linear estimators of multivariate regression coefficient with respect to an inequality constraint under matrix balanced loss function. Journal of Multivariate Analysis, 2014, 129: 37–43. (SCI) [9] He Daojiang*, Xu Xingzhong. A goodness-of-fit testing approach for normality based on the posterior predictive distribution. Test, 2013, 22: 1–18. (SCI)