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Course: STAT 27400=STAT 37400
Title: Nonparametric Inference
Instructor(s): John Lafferty
Course Assistant(s): Yuancheng Zhu
Class Schedule: Sec 01: TR 1:30–2:50PM in Ryerson 276
Office Hours: W 5:00-6:00 in E117
Course Homepage: Course Home Page
Textbook(s): Wasserman, All of Nonparametric Statistics
Description: Nonparametric inference is about developing statistical methods and models that make weak assumptions. A typical nonparametric approach estimates a nonlinear function from an infinite dimensional space, rather than a linear model from a finite dimensional space. This course gives an introduction to nonparametric inference, with a focus on density estimation, regression, confidence sets, orthogonal functions, random processes, and kernels. The course treats nonparametric methodology and its use, together with theory that explains the statistical properties of the methods.

Prerequisites are Stat 22400 or Stat 24400, or permission of the instructor.