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