When Statistics Embraces A.I.

Purdue Department of Statistics
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Publications

  1. Sun, X., Pang, D., Wang, X., and Ma, P. (2017). Optimal penalized function-on-function regression under a reproducing kernel Hilbert space. Journal of the American Statistical Association. In press.
  2. Wang, X. and Zhu, H. (2017). Generalized scalar-on-image regression models via total variation. Journal of the American Statistical Association. In press.
  3. Samel, K., Wang, X., and Liu, Q. (2017). A Neural Network Approach to Real Time Bidding. Journal of Purdue Undergraduate Research. In Press.
  4. Lebair, T., Shen, J., and Wang, X. (2017). Minimax lower bound and optimal estimation of convex functions in the sup-norm. IEEE Transactions on Automatic Control, 62, 3483-3487.
  5. He, S., Liu, C. and Wang, X. (2017). Modeling and inference of CD4 data. Statistical Modeling for Degradation Measurements. Springer.
  6. Qu, S., Wang, J.L. and Wang, X. (2016). Optimal estimation for the functional Cox model. Annals of Statistics, 44, 1708-1738.
  7. Wang, X. and Ruppert, D. (2015). Optimal prediction in an additive functional model. Statistica Sinica, 25, 567-590.
  8. Du, P. and Wang, X. (2014). Penalized likelihood functional regression, Statistica Sinica, 24, 1017-1041.
  9. Wang, X. and Shen, J. (2013). Uniform convergence and rate adaptive estimation of convex functions via constrained optimization. SIAM Journal of Control and Optimization, 51, 2753-2787.
  10. Wang, X., Du, P. and Shen, J. (2013). Smoothing splines with varying smoothing parameter. Biometrika, 100, 955-970.
  11. Choi, I., Li, B. and Wang, X. (2013). Nonparametric estimation of spatial and space-time covariance function, Journal of Agricultural, Biological, and Environmental Statistics, 4, 611-630.
  12. Li, B. and Wang, X. (2012). Discussion of ‘Clustering Random Curves Under Spatial Interde dependence with Application to Service Accessibility’ by H. Jiang and N. Serban,Technometrics, 54, 117-118.
  13. Shen, J. and Wang, X. (2012). Convex Regression via Penalized Splines: A Complementarity Approach. Proc. of 2012 American Control Conference, Montreal, Canada, June, 2012.
  14. Cheng, G. and Wang, X. (2011). Semiparametric additive transformation model under current status data, Electronic Journal of Statistics, 5, 1735-1764.
  15. Wang, X., Shen, J. and Ruppert, D. (2011). On the Asymptotics of Penalized Spline Smoothing, Electronic Journal of Statistics, 5, 1-17.
  16. Shen, J. and Wang, X. (2011). Estimation of Monotone Functions via P-Splines: A Constrained Dynamical Optimization Approach, SIAM Journal on Control and Optimization, 49, 646-671.
  17. Shen, J. and Wang, X. (2011).A Constrained Optimal Control Approach to Smoothing Splines, Proc. of the 50th IEEE Conf. on Decision and Control, 1729-1734, Orlando, FL, December, 2011.
  18. Wang, X. and Shen, J. (2010) A Class of Grouped Brunk Estimators and Penalized Spline Estimators for Monotone Regression. Biometrika, 97, 585-601.
  19. Wang, X. and Xu, D. (2010) An Inverse Gaussian Process Model for Degradation Data, Technometrics, 52, 188-197.
  20. Wang, X. (2010) Wiener Processes with Random Effects for Degradation Data, Journal of Multivariate Analysis, 101, 340-351.
  21. Shen, J. and Wang, X. (2010). Estimation of Shape Constrained Functions in Dynamical Systems and its Application to Genetic Networks . Proc. of 2010 American Control Conference, 5948-5953, Baltimore, MD.
  22. Wang, X. (2009) Semiparametric Inference on a Class of Wiener Processes, Journal of Time Series Analysis, 30, 179-207.
  23. Wang, X. (2009) Nonparametric Estimation of the Shape Function in a Gamma Process for Degradation Data, Canadian Journal of Statistics, 37. 101-118.
  24. Wang, X., Walker, M., Pal, J., Woodroofe, M., Mateo, M. (2008) Model-Independent Estimation of Dark Matter Distributions, Journal of the American Statistical Association, 103, 1070-1084.
  25. Wang, X. (2008) A Pseudo-Likelihood Estimation Method for Nonhomogeneous Gamma Process Model with Random Effects, Statistica Sinica, 18, 1153-1163.
  26. Wang, X. (2008) Bayesian Free-knot Monotone Cubic Spline Regression, Journal of Computational and Graphical Statistics, 17, 373-387.
  27. Wang, X. and Li, F. (2008) Isotonic Smoothing Spline Regression, Journal of Computational and Graphical Statistics, 17, 21-37.
  28. Wang, X., Woodroofe, M., Pal, J., Walker, M. and Mario, M. (2007). Nonparametric Estimation of Dark Matter Distributions, Statistical Challenges in Modern Astronomy IV (Editors:G. J. Babu and E. D. Feigelson), 371, 268-279.
  29. Wang, X. (2007). Physical Degradation Models, Encyclopedia of Statistics in Quality andReliability, Ruggeri, F., Kenett, R. and Faltin, F.W. (eds). 1356-1361.
  30. Wang, X. and Woodroofe, M. (2007) A Kiefer Wolfowitz Comparison Theorem for Wichsell's Problem, Annals of Statistics, 35, 1559-1575.
  31. Walker, M., Mateo, M., Olszewskia,E., Gnedini, O., Wang, X., Sen, B, Woodroofe, M. (2007) Velocity Dispersion Pro_les of Seven Dwarf Spheroidal Galaxies, Astrophysical Journal Letters, 667, L53-L56.
  32. Walker, M., Mateo, M., Olszewski, E., Wang, X. and Woodroofe M. (2006) Radial Velocity Dispersion Profile in the Fornax Dwarf Spheroidal Galaxy, The Astronomical Journal, 131, 2114-2139.
  33. Wang, X., Woodroofe, M., Walker, M., Mateo, M. and Olszewski, E. (2005) Estimating Dark Matter Distributions, The Astrophysical Journal, 626, 145-158.
  34. Nair, V. and Wang, X. (2004) Discussion of `Failure Amplification Method: An Information Maximization Approach to Categorical Response Optimization' by Joseph and Wu,Technometrics 46, 19-23.
  35. Cheng, Y., He, J., Wang, X. (2000) Gauge Transformation to solve (m,n)th KdV Hierarchy, Journal of University of Science and Technology of China, 30, 507-516.