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May 1, 1989Technometrics6,197 citations

Robust Regression and Outlier Detection

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GPGregory F. PiepelPRPeter J. RousseeuwALAnnick M. Leroy

Key Points

  • The aim is to explore robust regression models and outlier detection diagnostics to enhance data analysis.
  • Discussion of simple and multiple regression techniques
  • Examination of one-dimensional location cases
  • Analysis of algorithms for detecting outliers
  • Introduction of various robust regression algorithms
  • Identification of key outlier diagnostics
  • Overview of related statistical techniques

Abstract

Introduction. 2. Simple Regression. 3. Multiple Regression. 4. The Special Case of One-Dimensional Location. 5. Algorithms. 6. Outlier Diagnostics. 7. Related Statistical Techniques. References. Table of Data Sets. Index.

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Cite This Study

Piepel et al. (1989) studied this question.

synapsesocial.com/papers/6a07db58f74749d21579f8a3https://doi.org/10.2307/1268828
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