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| Autor/in | Lane, Ken |
|---|---|
| Titel | What Is Robust Regression and How Do You Do It? |
| Quelle | (2002), (16 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Estimation (Mathematics); Regression (Statistics); Robustness (Statistics) |
| Abstract | All parametric statistical analyses have certain assumptions about the data that must be met reasonably to warrant the use of a given analysis. Distributional normality, for example, is a common assumption. There is a variety of ways that data in a distribution may detract from normality, but one common problem is the presence of outliers. Many applied regression researchers, however, are unfamiliar with the potential role and process of robust regression procedures. Robust regression methods attempt to minimize the impact of outliers on regression estimators, but still invoke parametric assumptions after smoothing the influence of outliers on the slope and intercept. The purpose of this paper is to discuss and demonstrate several robust regression techniques. The paper demonstrates the impact of outliers on regression estimators, discusses several common robust techniques, and illustrates the trimmed least squares and MM robust techniques using the S-PLUS statistical software package. A heuristic data set is used to make the discussion concrete and accessible to readers. (Contains 1 table, 8 figures, and 17 references.) (Author/SLD) |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
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