Skip to main content

Upper and Lower Bounds for Breakdown Points

  • Chapter
Robustness and Complex Data Structures
  • 2050 Accesses

Abstract

General upper and lower bounds for the finite sample breakdown point are presented. The general upper bound is obtained by an approach of Davies and Gather using algebraic groups of transformations. It is shown that the upper bound for the finite sample breakdown point has a simpler form than for the population breakdown point. This result is applied to multivariate regression. It is shown that the upper bounds of the breakdown points of estimators of regression parameters, location and scatter can be obtained with the same group of transformations. The general lower bound for the breakdown point of some estimators is given via the concept of d-fullness introduced by Vandev. This provides that the lower bound and the upper bound can coincide for least trimmed squares estimators for multivariate regression and simultaneous estimation of scale and regression parameter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Davies, P. L. (1993). Aspects of robust linear regression. The Annals of Statistics, 21, 1843–1899.

    Article  MathSciNet  MATH  Google Scholar 

  • Davies, P. L., & Gather, U. (2005). Breakdown and groups (with discussion). The Annals of Statistics, 33, 977–1035.

    Article  MathSciNet  MATH  Google Scholar 

  • Davies, P. L., & Gather, U. (2007). The breakdown point—examples and counterexamples. REVSTAT Statistical Journal, 5, 1–17.

    MathSciNet  MATH  Google Scholar 

  • Donoho, D. L., & Gasko, M. (1992). Breakdown properties of location estimates based on halfspace depth and projected outlyingness. The Annals of Statistics, 20, 1803–1827.

    Article  MathSciNet  MATH  Google Scholar 

  • Donoho, D. L., & Huber, P. J. (1983). The notion of breakdown point. In P. J. Bickel, K. Doksum, & J. L. Hodges (Eds.), A Festschrift for Erich L. Lehmann (pp. 157–184). Belmont: Wadsworth.

    Google Scholar 

  • Hampel, F. R. (1971). A general qualitative definition of robustness. The Annals of Mathematical Statistics, 42, 1887–1896.

    Article  MathSciNet  MATH  Google Scholar 

  • Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust statistics. The approach based on influence functions. New York: Wiley.

    MATH  Google Scholar 

  • Müller, Ch. H. (1995). Breakdown points for designed experiments. Journal of Statistical Planning and Inference, 45, 413–427.

    Article  MathSciNet  MATH  Google Scholar 

  • Müller, Ch. H. (1997). Lecture notes in statistics: Vol. 124. Robust planning and analysis of experiments. New York: Springer.

    Book  MATH  Google Scholar 

  • Müller, Ch. H., & Neykov, N. (2003). Breakdown points of trimmed likelihood estimators and related estimators in generalized linear models. Journal of Statistical Planning and Inference, 116, 503–519.

    Article  MathSciNet  MATH  Google Scholar 

  • Müller, Ch. H., & Schäfer, Ch. (2010). Designs with high breakdown point in nonlinear models. In A. Giovagnoli, A. C. Atkinson, & B. Torsney (Eds.), mODa 9—advances in model-oriented design and analysis (pp. 137–144). Heidelberg: Physica-Verlag.

    Chapter  Google Scholar 

  • Neykov, N., & Müller, Ch. H. (2003). Breakdown point and computation of trimmed likelihood estimators in generalized linear models. In R. Dutter, P. Filzmoser, U. Gather, & P. J. Rousseeuw (Eds.), Developments in robust statistics (pp. 277–286). Heidelberg: Physica-Verlag.

    Chapter  Google Scholar 

  • Rousseeuw, P. J., & Leroy, A. M. (2003). Robust regression and outlier detection. New York: Wiley.

    Google Scholar 

  • Rousseeuw, P. J., & Driessen, K. (2006). Computing LTS regression for large data sets. Data Mining and Knowledge Discovery, 12, 29–45.

    Article  MathSciNet  Google Scholar 

  • Vandev, D. L. (1993). A note on breakdown point of the least median squares and least trimmed squares. Statistics & Probability Letters, 16, 117–119.

    Article  MathSciNet  MATH  Google Scholar 

  • Vandev, D., & Neykov, N. (1998). About regression estimators with high breakdown point. Statistics, 32, 111–129.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christine H. Müller .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Müller, C.H. (2013). Upper and Lower Bounds for Breakdown Points. In: Becker, C., Fried, R., Kuhnt, S. (eds) Robustness and Complex Data Structures. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35494-6_5

Download citation

Publish with us

Policies and ethics