Yong Wang

Office: 303.271 (Maths/Physics Building, Science Centre)
Phone: +64 9 3737599 ext. 84700
E-mail: yongwang AT auckland.ac.nz

Teaching

• 783 2013FC

Selected Publications

• Wang, Y. and Taylor, S. M. (2013). Efficient computation of nonparametric survival functions via a hierarchical mixture formulation. Statistics and Computing, (Accepted).
• Chee, C.-S. and Wang, Y. (2013). Estimation of finite mixtures with symmetric components. Statistics and Computing, 23, 233-249.
• Chee, C.-S. and Wang, Y. (2013). Minimum quadratic distance density estimation using nonparametric mixtures. Computational Statistics & Data Analysis, 57, 1-16.
• Wang, Y. (2012). Gauss-Newton method. Wiley Interdisciplinary Reviews: Computational Statistics, 4, 415-420.
• Wang, Y., Ziedins, I., Holmes, M. and Challands, N. (2012). Tree models for difference and change detection in a complex environment. Annals of Applied Statistics, 6, 1162-1184.
• Wang, Y. and Chee, C.-S. (2012). Density estimation using nonparametric and semiparametric mixtures. Statistical Modelling: An International Journal, 12, 67-92.
• Blagojevic, R., Plimmer, B., Grundy, J. and Wang, Y. (2011). Using data mining for digital ink recognition: Dividing text and shapes in sketched diagrams. Computers & Graphics, 35, 976-991.
• Böhning, D. and Wang, Y. (2010) Comments on "Maximum likelihood estimation of a multivariate log-concave density" by Cule, Samworth and Stewart. Journal of the Royal Statistical Society, Series B, 72, 589-590.
• Wang, Y. (2010). Fisher scoring: an interpolation family and its Monte Carlo implementations. Computational Statistics & Data Analysis, 54, 1744-1755.
• Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric mixture models. Statistics and Computing, 20, 75-86.
• Blagojevic, R., Plimmer, B., Grundy, J. and Wang, Y. (2010). Building digital ink recognizers using data mining: Distinguishing between text and shapes in hand drawn diagrams. Lecture Notes in Computer Science, 6096, 358-367.
• Wang, Y. (2009). The constrained Fisher scoring method for maximum likelihood computation of a nonparametric mixing distribution. Computational Statistics, 24, 67-81.
• Wang, Y. (2008). Dimension-reduced nonparametric maximum likelihood computation for interval-censored data. Computational Statistics & Data Analysis, 52, 2388-2402.
• Blagojevic, R., Plimmer, B., Grundy, J. and Wang, Y. (2008). Development of techniques for sketched diagram recognition. Proceedings of the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing, 258-259.
• Wang, Y. (2007). Minimum disparity computation via the iteratively reweighted least integrated squares algorithms. Computational Statistics & Data Analysis, 51, 5662-5672.
• Wang, Y. (2007). Maximum likelihood computation based on the Fisher scoring and Gauss-Newton quadratic approximations. Computational Statistics & Data Analysis, 51, 3776-3787.
• 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, 69, 185-198.
• Wang, Y. and Witten, I. H. (2002). Modeling for optimal probability prediction. Proceedings of the Nineteenth International Conference on Machine Learning, 650-657. Morgan Kaufmann.
• Frank, E., Wang, Y., Inglis, S., Holmes, G. and Witten, I. H. (1998). Using model trees for classification. Machine Learning, 32, 63-76.

• Wang, Y. (2000). A New Approach to Fitting Linear Models in High Dimensional Spaces. PhD thesis, Department of Computer Science, University of Waikato, New Zealand.

Computer Programs

• The constrained Newton method for computing the NPMLE of a mixing distribution
  • The NNLS algorithm is available in the LSEI package below.
  • Readme | cnm.R | (Blackwell Publishing)
• LSEI package (2007-May-28): lsei_1.0-2.tgz (Source code) | lsei_1.0-2.zip (Compiled for Windows)
  • Contains Lawson and Hanson's (1995) Fortran program (downloaded from here) and some R functions for solving linear regression problems with equality and inequality constraints.