Carnegie Mellon University

Nonparametric Methods

Non-parametric (or distribution-free) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the probability distributions of the variables being assessed. These methods are particularly useful for analyzing complex datasets where traditional assumptions may not hold, allowing for greater flexibility and robustness in estimation and inference.

Faculty with this Research Interest

15 bios displayed.

Siva Balakrishnan

Siva Balakrishnan

Associate Professor (on leave Spring 2025)

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Eli Ben-Michael

Eli Ben-Michael

Assistant Professor, joint with Heinz College

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Zach Branson

Zach Branson

Assistant Teaching Professor and Assistant Director for the Undergraduate Program

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Edward Kennedy

Edward Kennedy

Associate Professor

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Ann Lee

Ann Lee

Professor, Co-Director Ph.D. Program

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Jing Lei

Jing Lei

Professor (on leave Spring 2025)

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Gonzalo Mena

Gonzalo Mena

Assistant Professor

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Kathryn  Roeder

Kathryn Roeder

UPMC Professor of Statistics and Life Sciences

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Cosma Shalizi

Cosma Shalizi

Associate Professor

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Yandi Shen

Yandi Shen

Assistant Professor

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Weijing Tang

Weijing Tang

Assistant Professor

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Larry Wasserman

Larry Wasserman

UPMC University Professor (on leave Spring 2025)

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