You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. 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
Joseph W. McKean
Vol. 19, No. 4 (Nov., 2004), pp. 562-570
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/4144426
Page Count: 9
You can always find the topics here!Topics: Estimators, Linear models, Statistical estimation, Datasets, Mathematical vectors, Analytical estimating, Point estimators, Statistics, Statistical models, Geometry
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
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.
Preview not available
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.
Statistical Science © 2004 Institute of Mathematical Statistics