Statistics (Nonparametrics and Robustness) OpenCourseWare: MIT's Free Online Graduate Level Statistics Course
'Topics in Statistics: Nonparametrics and Robustness' is a graduate-level OpenCourseWare presented by the Massachusetts Institute of Technology (MIT). The course takes a relatively modern approach to one-dimensional nonparametric statistics. Previous coursework in statistics for applications or statistical inference is suggested.
Topics in Statistics: Nonparametrics and Robustness: Course Specifics
| Degree Level | Free | Audio | Video | Downloads |
|---|---|---|---|---|
| Graduate | Yes | No | No | Yes |
| Lectures/Notes | Study Materials | Tests/Quizzes |
|---|---|---|
| Yes | Yes | No |
Topics in Statistics: Nonparametrics and Robustness: Course Description
Originally presented by MIT's Professor Richard Dudley, 'Topics in Statistics: Nonparametrics and Robustness' follows a method which uses rotationally or affine invariant procedures the method involving fixed coordinate system. This is generally considered a more modern approach to this challenging subject. A series of lectures and readings explore various statistical topics, including outliers, the Delta-Method and asymptotic estimators, robustness, the spatial median and location and scatter functionalities. A series of eight problem sets help students delve deeper into these topics. Recommended prerequisites for the course include statistics for applications or statistical inference. Several of the lectures are based on recommended text books listed in the syllabus.
This OpenCourseWare includes lecture notes and problem sets. If you are interested in completing this course, visit the statistics in nonparametrics and robustness course web page.
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