2014年学术论文

发布者:张建涛发布时间:2015-10-23浏览次数:508

  1. Zou Changliang, Peng Liuhua, and Wang Zhaojun*(2014). Spatial-signs based on high dimensional tests for shericity. Biometrika, 101(1), 229-236. 

  2. Zou Changliang, Yin Guosheng, Feng Long, and Wang Zhaojun* (2014). Nonparametric maximum likelihood approach to multiple change-point problems.  Annals of Statistics, 42(3), 790-1002.

  3. Zou Changliang, Tseng Shengtsaing, and Wang Zhaojun* (2014). Outlier detection in general profiles using penalized regression method.  IIE Transactions, 46, 106-117.

  4. Chen Guanglei, and Wang Zhaojun (2014). The multivariate partially linear EV model with B-spline. Chinese Journal of Applied Probability and Statistics, Vol 29(6), 581-592.   

  5. Li Zhonghua, Dai Yi, and Wang Zhaojun* (2014). Multivariate change point control chart based on data depth for phase I analysis. Communication in Statistics-Simulation and Computation. Accepted.

  6. Guo Hong, Zou Changliang, and Wang Zhaojun* , Chen Bin (2014). Empirical likelihood for high dimensional linear regression model, Metrika, 77(7), 921-945. 

  7. Li Zhonghua, Zou Changliang, Gong Zhen, and Wang Zhaojun* (2014). The Computation of Average Run Length and Average Time to Signal: An Overview, Journal of Statistical Computation and Simulation, accepted. 84(8), 1779-1802.

  8. Zhou Maoyuan, Geng Wei and Wang Zhaojun*(2014). Likelihood Ratio-Based Distribution-Free Sequential Change-Point Detection, Journal of Statistical Computation and Simulation, accepted.

  9. Zhang Jiujun, Li Zhonghua, Zhou Qin, and Wang Zhaojun*(2014). An adaptive Shiryaev-Roberts procedure for signalling varying location shifts. Communication in Statistics-Simulation and Computation. Accepted

  10. Feng Long, Zou Changliang, Wang Zhaojun* , Wei Xianwu, and Chen Bin (2014). Robust Spline-Based Variable Selection in Varying Coefficient Model.Metrika, accepted.

  11. Feng Long, Zou Changliang, Wang Zhaojun, and Zhu Lixing (2014). Robust comparison of regression curves. Test, on-line published.

  12. Zhang Jiujun, Li Zhonghua, and Wang Zhaojun*(2014). Likelihood Ratio Test-Based Chart for Monitoring the Process Variability, Communications in Statistics - Simulation and Computation, accepted.

  13. Zhang Jiujun, Li Zhonghua, Chen Bin and Wang Zhaojun*(2014). A new exponentially weighted moving average control chart for monitoring the coefficient of variation. Computer & Industrial Engineering, 78, 205-212. 

  14. Feng Long,Zou ChangliangWang Zhaojun and Zhu Lixing.(2014). Two Sample Behrens-Fisher problem for high-dimensional data. StatisticaSinica, accepted

  15. Qi Dequan, Li Zhonghua, ZiXuemin, ad Wang Zhaojun* (2014). Monitoring data quality based on conditional false discovery rate. Quality Technology and Quantitative Management.

  16. Georgiou, S.D., Koukouvinos, C. and Liu, M. Q. (2014). U-type and column-orthogonal designs for computer experiments. Metrika 77(8), 1057-1073. [SCI]

  17. Yang, X., Chen, H. and Liu, M. Q. (2014). Resolvable orthogonal array-based uniform sliced Latin hypercube designs. Statist. Probab. Lett. 93, 108-115. [SCI]

  18. Yang, J. Y., Lin, D. K. J. and Liu, M. Q. (2014). Construction of minimal-point mixed-level screening designs using conference matrices. J. Qual. Technol. 46(3), 251-264. [SCIEI]

  19. Sun, F. S., Liu, M. Q. and Qian, P. Z. G. (2014). On the construction of nested space-filling designs. Annals of Statistics 42(4), 1394-1425. [SCI]

  20. Yin, Y. H., Lin, D. K. J. and Liu, M. Q. (2014). Sliced Latin hypercube designs via orthogonal arrays. J. Statist. Plann. Inference 149, 162-171. [SCI]

  21. Huang, H. Z., Yang, J. F. and Liu, M. Q. (2014). Construction of sliced (nearly) orthogonal Latin hypercube designs. J. Complexity 30(3), 355-365. [SCIEI]

  22. Huang, H. Z., Yang, J. Y. and Liu, M. Q. (2014). Functionally induced priors for componentwise Gibbs sampler in the analysis of supersaturated designs. Comput. Statist. Data Anal. 72, 1-12.[SCIEI]

  23. Yang, J. Y., Liu, M. Q. and Lin, D. K. J. (2014). Construction of nested orthogonal Latin hypercube designs. Statist. Sinica 24(1), 211-219. [SCI]

     

 

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