Indiana University Bloomington

2016

Statistics S625
Nonparametric Theory And Data Analysis

Contact: Qingsong Shan qingshan@indiana.edu
Class Time: 4:00-5:15 BH 233
Class Days: Tu, Th
Pre-Requisites: College-level probability (e.g. M463) and statistics with computing (e.g. S520)
Algebra Required: Some notion of matrix algebra is useful
Calculus Required: Some notion of integration is useful
Instructor: Qingsong Shan
Recommended follow-up classes: Machine learning
Syllabus: Download Syllabus
Keywords: Nonparametric, bootstrap, permutation test, density estimation, curve estimation
Description: Survey of methods for statistical inference that do not rely on parametric probability models. Statistical functionals, bootstrapping, empirical likelihood. Nonparametric density and curve estimation. Rank and permutation tests.
Books: Lecture Notes.
Applied/Theoretical: Somewhat more applied than theoretical
Formal Computing Lab: No
Software: R
How Software is Used: The software is mainly used for computation and data analysis
Problem Sets: Weekly
Data Analysis: Yes
Presentation: No
Exams: Yes
Comments: The course is an introduction to statistics outside of the "classical" techniques. Over and above the material itself, the course is useful for reinforcement of and elaboration on concepts of testing and estimation seen in classical courses, and serves as a bridge to modern, computationally intensive branches of statistics like machine learning.