Topics in Statistics: Nonparametrics and Robustness

Graph of two sample data with an outlier.

Two-sample data with an outlier from Problem set 1, problems 4-5. (Image courtesy of OCW.)


MIT Course Number


As Taught In

Spring 2005



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Course Description

Course Features

Course Description

This graduate-level course focuses on one-dimensional nonparametric statistics developed mainly from around 1945 and deals with order statistics and ranks, allowing very general distributions.

For multidimensional nonparametric statistics, an early approach was to choose a fixed coordinate system and work with order statistics and ranks in each coordinate. A more modern method, to be followed in this course, is to look for rotationally or affine invariant procedures. These can be based on empirical processes as in computer learning theory.

Robustness, which developed mainly from around 1964, provides methods that are resistant to errors or outliers in the data, which can be arbitrarily large. Nonparametric methods tend to be robust.

Other Versions

Other OCW Versions

This is a graduate-level subject in Statistics. The content varies year to year, according to the interests of the instructor and the students.

Related Content

Richard Dudley. 18.465 Topics in Statistics: Nonparametrics and Robustness. Spring 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, License: Creative Commons BY-NC-SA.

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