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May 1, 1987Technometrics3,795 citations

Robust Statistics: The Approach Based on Influence Functions

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DRDavid RuppertCornell UniversityFHFrank R. HampelFederal Statistical OfficeERElvezio RonchettiUniversity of Geneva

Key Points

  • The research aims to evaluate the application of robust statistical methods, particularly focusing on influence functions for estimation and testing.
  • Review of one-dimensional and multidimensional estimators and tests.
  • Analysis of robust estimation techniques for linear models.
  • Discussion on the estimation of covariance matrices and multivariate locations.
  • Identifies advantages of robust estimators in handling outliers.
  • Demonstrates improved validity of tests using influence functions in multidimensional spaces.
  • Highlights effective methods for robust testing in linear models.

Abstract

Introduction and Motivation. 2. One-Dimensional Estimators. 3. One-Dimensional Tests. 4. Multidimensional Estimators. 5. Estimation of Covariance Matrices and Multivariate Location. 6. Linear Models: Robust Estimation. 7. Linear Models: Robust Testing. 8. Complements and Outlook. References. Index.

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Cite This Study

Ruppert et al. (1987) studied this question.

synapsesocial.com/papers/6a07db58f74749d21579f8a4https://doi.org/10.2307/1269782
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