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Robust Analysis of Linear Models

Joseph W. McKean
Statistical Science
Vol. 19, No. 4 (Nov., 2004), pp. 562-570
Stable URL: http://www.jstor.org/stable/4144426
Page Count: 9
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Robust Analysis of Linear Models
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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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