•Huang,X., Goldberg,Y. and Xu,J. (2019) Multi-category individualized treatment regime using outcome weighted learning, Biometrics, in press.
•Mu,R., Yuan,Y., Xu,J., Mandrekar,S.J. and Yin,J. (2019) gBOIN: a unified model-assisted phase I trial design accounting for toxicity grades, and binary or continuous end points, Journal of the Royal Statistical Society Series C, 68, 289-308.
•Zhang,L. and Xu,J. (2018) Crossover design with two binary endpoints, Statistics in Biopharmaceutical Research, 10, 316-322.
•Xiong,C. and Xu,J. (2018) Efficient Robbins-Monro procedure for multivariate binary data, Statistical Theory and Related Fields, 2, 172-180.
•Mu,R. and Xu,J. (2018) Predicting events in clinical trials using two time-to-event outcomes, Biometrical Journal, 60, 815-826.
•Mu,R. and Xu,J. (2017) A new Bayesian dose finding design for drug combination trials, Statistics in Biopharmaceutical Research, 9, 384-389.
•Jiang,Y. and Xu,J. (2017) A comparative study of matched pair designs with two binary endpoints, Statistical Methods in Medical Research, 26, 2526-2542.
•Mu,R., Dai,L., and Xu,J. (2017) Sequential design for response surface model fit in computer experiments using derivative information, Communications in Statistics –Simulation and Computation, 47, 1148-1155.
•Harrar,S. and Xu,J. (2016) Confidence regions for level differences in growth curve models, Journal of Statistical Planning and Inference, 175, 11-24.
•Xiong,C. and Xu,J. (2016) Confidence intervals from stochastic approximation, Communications in Statistics –Simulation and Computation, 45, 1827-1837.
•Xu,J., Chen,J., and Qian,P.Z.G. (2015) Sequentially refined Latin hypercube designs: reusing every point, Journal of the American Statistical Association, 110, 1696-1706.