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New Statistical Robust Estimators, Open Problems

In: Open Problems in Optimization and Data Analysis

Author

Listed:
  • George Zioutas

    (Aristotle University of Thessaloniki)

  • Chris Chatzinakos

    (Bio-Technology Research Park)

  • Athanasios Migdalas

    (Lulea University of Technology
    Aristotle University of Thessaloniki)

Abstract

The goal of robust statistics is to develop methods that are robust against outliers in the data. We emphasize on high breakdown estimators, which can deal with heavy contamination in the data set. We give an overview of recent popular robust methods and present our new approach using operational research techniques, like mathematical programming. We present some open problems of the new robust procedures for improving robustness and efficiency of the proposed estimators.

Suggested Citation

  • George Zioutas & Chris Chatzinakos & Athanasios Migdalas, 2018. "New Statistical Robust Estimators, Open Problems," Springer Optimization and Its Applications, in: Panos M. Pardalos & Athanasios Migdalas (ed.), Open Problems in Optimization and Data Analysis, pages 23-47, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-99142-9_3
    DOI: 10.1007/978-3-319-99142-9_3
    as

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