Statistics 598M Final Project

The final project is a paper presentation. Every group is required to read a paper, understand the theory, methodology and examples in the paper, and present it to the class. You also need to hand in a brief summary of the paper with your comments after presentation. A list of the possible topics and papers is give below. You can choose a paper from the list, and email me your choice. If two groups have chosen the same paper, it will be given to the group that has sent me the choice first. You can also choose a topic or a paper outside this list, however, you need send me the paper and get my approval to use it for the project.

The presentation is 25 minutes. Two groups will be scheduled on Friday, May 2, in class, and the rest four groups on Tuesday, May 6, 10:20-12:20pm.

Topics and Papers

Dimension Reduction and Regression

  • Zhenyue Zhang and Hongyuan Zha Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment (2002)
  • Chris Ding, Xiaofeng He, Hongyuan Zha, Horst Simon, Adaptive dimension reduction for clustering high dimensional data
  • Sanjeev Arora, Ravi Kannan Learning mixtures of arbitrary gaussians (2001)
  • Avner Magen Dimensionality Reductions that Preserve Volumes and Distance to Affine Spaces, and their Algorithmic Applications
  • Bura, E. and Cook, R. D. (2001). Estimating the structural dimension of regressions via parametric inverse regression, Journal of the Royal Statistical Society 63, 393-410.
  • Yingcun Xia, Howell Tong, W. K. Li, Li-Xing Zhu An adaptive estimation of dimension reduction space,
    • JRSSB,(2002)
  • Ker-Chau Li Nonlinear Confounding in High-Dimensional RegressionAnnals,(1997)

    • Bayesian Trees

  • Chipman, H., George, E., and McCulloch, R. (1998) ``Bayesian CART Model Search (with discussion)'', Journal of the American Statistical Association, 93, 935--960
  • Hugh A. Chipman, Edward I. George, Robert E. McCulloch Bayesian Treed Models, Machine Learning,2002

    • Spline

  • Charles Kooperberg; Smarajit Bose; Charles J. Stone Polychotomous Regression (in Theory and Methods),Journal of the American Statistical Association, Vol. 92, No. 437. (Mar., 1997), pp. 117-127.
  • Charles J. Stone; Mark H. Hansen; Charles Kooperberg; Young K. Truong Polynomial Splines and their Tensor Products in Extended Linear Modeling. Annals of Statistics, Vol. 25, No. 4. (Aug., 1997), pp. 1371-1425
  • Hansen and Kooperberg Spline Adaptation in Extended Linear Models, Statistical Science, 17, 20-21

    • Boosting

  • Y. Freund. An adaptive version of the boost by majority algorithm. Machine Learning, 43(3):293-318, June 2001
  • G. Ratsch, A. Demiriz, and K. Bennett Sparse regression ensembles in infinite and finite hypothesis spaces. Machine Learning, 48(1-3):193-221, 2002
  • H. Drucker Effect of pruning and early stopping stopping on performance of a boosted ensemble. In Proceedings of the International Meeting on Nonlinear Methods and Data Mining, pages 26-40, Rome, Italy, 2000
  • R.E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, October 1998

    • Support Vector Machine

  • Lee, Y., Lin, Y. and Wahba, G. (2002) Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data. Revision invited. Journal of American Statistical Association
  • O. L. Mangasarian and D. R. Musicant. Data discrimination via nonlinear generalized support vector machines. Technical Report 99-03, University of Wisconsin, Computer Sciences Department, Madison, WI, USA, 1999
  • B. Scholkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Technical Report 99-87, Microsoft Research, 1999. To appear in Neural Computation, 2001.
  • Y. Lin, G. Wahba, H. Zhang, and Y. Lee. Statistical properties and adaptive tuning of support vector machines. Department of statistics technical report 1022, University of Wisconsin-Madison, 2000.
  • Bernhard Scholkopf, Alex J. Smola, Robert C. Williamson, and Peter L. Bartlett New Support Vector Algorithms Neural Comp. 2000 12: 1207-1245