Dr Yong Wang

BEng/MEng (Huazhong University of Science and Technology), PhD (Waikato University)

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Senior Lecturer

Biography

Yong Wang joined the Department of Statistics in 2003. He received B.Eng. and M.Eng. degrees from Huazhong University of Science and Technology in 1986 and 1989 and Ph.D. degree in Computer Science in 2001 from Waikato University.

Research | Current

Dr Wang's research interests include

  • Computational statistics
  • Mixture models
  • Survival analysis
  • Data mining.

Selected publications and creative works (Research Outputs)

  • Chee, C. S., & Wang, Y. (2016). Nonparametric estimation of species richness using discrete k-monotone distributions. Computational Statistics and Data Analysis, 93, 107-118. 10.1016/j.csda.2014.10.021
  • Wang, X., & Wang, Y. (2015). Nonparametric multivariate density estimation using mixtures. Statistics and Computing, 25 (2), 349-364. 10.1007/s11222-013-9436-y
  • Wang, Y., & Taylor, S. M. (2013). Efficient computation of nonparametric survival functions via a hierarchical mixture formulation. Statistics and Computing, 23 (6), 713-725. 10.1007/s11222-012-9341-9
  • Wang, Y. (2012). Gauss-Newton method. Wiley Interdisciplinary Reviews: Computational Statistics, 4 (4), 415-420. 10.1002/wics.1202
  • Wang, Y., Ziedins, I., Holmes, M., & Challands, N. (2012). Tree models for difference and change detection in a complex environment. Annals of Applied Statistics, 6 (3), 1162-1184. 10.1214/12-AOAS548
  • Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric mixture models. Statistics and Computing, 20 (1), 75-86. 10.1007/s11222-009-9117-z
    URL: http://hdl.handle.net/2292/14339
  • Wang, Y. (2008). Dimension-reduced nonparametric maximum likelihood computation for interval-censored data. Computational Statistics and Data Analysis, 52 (5), 2388-2402. 10.1016/j.csda.2007.10.018
    URL: http://hdl.handle.net/2292/14341
  • Wang, Y. (2007). On fast computation of the non-parametric maximum likelihood estimate of a mixing distribution. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 69 (2), 185-198. 10.1111/j.1467-9868.2007.00583.x
    URL: http://hdl.handle.net/2292/14340