Abstract
Part of a linear model analysis is the examination of the appropriateness of the chosen model. We propose an exploratory model criticism procedure that exposes hidden outliers, clusters of outliers, or underlying curvature by using diagnostics that exploit the differences between an efficient robust fit and a high breakdown fit. Examples illustrate the procedure.
Original language | English |
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Pages (from-to) | 2575-2595 |
Number of pages | 21 |
Journal | Communications in Statistics - Theory and Methods |
Volume | 25 |
Issue number | 11 |
DOIs | |
State | Published - 1996 |
Keywords
- Diagnostics
- GR-Estimates
- Influential points
- Outliers
- R-Estimates
- Residual plots
- Robust
- Wilcoxon-Estimates
ASJC Scopus subject areas
- Statistics and Probability