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November 2004 Robust Analysis of Linear Models
Joseph W. McKean
Statist. Sci. 19(4): 562-570 (November 2004). DOI: 10.1214/088342304000000549

Abstract

This paper presents three lectures on a robust analysis of linear models. One of the main goals of these lectures is to show that this analysis, similar to the traditional least squares-based analysis, offers the user a unified methodology for inference procedures in general linear models. This discussion is facilitated throughout by the simple geometry underlying the analysis. The traditional analysis is based on the least squares fit which minimizes the Euclidean norm, while the robust analysis is based on a fit which minimizes another norm. Several examples involving real data sets are used in the lectures to help motivate the discussion.

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Joseph W. McKean. "Robust Analysis of Linear Models." Statist. Sci. 19 (4) 562 - 570, November 2004. https://doi.org/10.1214/088342304000000549

Information

Published: November 2004
First available in Project Euclid: 18 April 2005

zbMATH: 1100.62583
MathSciNet: MR2185577
Digital Object Identifier: 10.1214/088342304000000549

Rights: Copyright © 2004 Institute of Mathematical Statistics

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Vol.19 • No. 4 • November 2004
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