[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)
