Stat 6550 Nonparametric regression


Non-parametric regression techniques and concepts such as splines, kernels, regularization, and cross-validation are important for the development and understanding of modern machine/statistical learning tachniques and also extremely useful and flexible tools for data analysis. Students in this course will learn how to use non-parametric techniques to analyze data and how the methods work. The course will focus on practical methods for data analysis, not just theory.

Tentative topics to be covered include:

  • Estimating the CDF and statistical functionals.
  • Bootstrap and Jacknife
  • Kernel smoothing
  • Density estimation
  • Penalized regression; splines
  • Semiparametric and wavelet regression


Undergraduate course in regression and Stat-6510.


  • Semiparametric regression by Ruppert, Wand, and Carroll


Mathematics and Statistics

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