GS01 1273 Modern Nonparametrics
Wei, Peng; Li, Yisheng; and Wang, Jian. Three semester hours. Spring, biennial. Grading System: Letter Grade. Prerequisites: GS01 1083: Mathematical Statistics I (or equivalent) and Linear Regression or Consent of Instructor.
This course seeks to introduce students to the many developments in modern nonparametrics, including resampling methods, nonparametric and semiparametric regression models that have occurred over the last several decades. Topics include the bootstrap, jackknife, cross-validation, permutation tests, classification tree, random forests, nonparametric smoothing and regression, spline regression, and functional data analysis. While the course will focus on applications, time will be devoted to derivations and theoretical justifications of methods. The statistical software R will be used for the homework exercises.