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Analyzing the Number of Samples Required for an Approximate Monte-Carlo LMS Line Estimator

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Theory and Applications of Recent Robust Methods

Part of the book series: Statistics for Industry and Technology ((SIT))

Abstract

This paper analyzes the number of samples required for an approximate Monte-Carlo least median of squares (LMS) line estimator. We provide a general computational framework, followed by detailed derivations for several point distributions and subsequent numerical results for the required number of samples.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Maryland, College Park, MD, 20742, USA

    D. M. Mount

  2. Department of Computer Science, Bar-Ilan University, Ramat-Gan, 52900, Israel

    N. S. Netanyahu & E. Zuck

  3. Center for Automation Research, University of Maryland, College Park, MD, 20742, USA

    N. S. Netanyahu

Authors
  1. D. M. Mount
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  2. N. S. Netanyahu
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  3. E. Zuck
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Katholieke Universiteit Leuven, W. de Croylaan 54, 3001, Leuven, Belgium

    Mia Hubert

  2. Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 S9, 9000, Gent, Belgium

    Greet Pison  & Stefan Van Aelst  & 

  3. Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium

    Anja Struyf

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© 2004 Springer Basel AG

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Mount, D.M., Netanyahu, N.S., Zuck, E. (2004). Analyzing the Number of Samples Required for an Approximate Monte-Carlo LMS Line Estimator. In: Hubert, M., Pison, G., Struyf, A., Van Aelst, S. (eds) Theory and Applications of Recent Robust Methods. Statistics for Industry and Technology. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-7958-3_19

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  • DOI: https://doi.org/10.1007/978-3-0348-7958-3_19

  • Publisher Name: Birkhäuser, Basel

  • Print ISBN: 978-3-0348-9636-8

  • Online ISBN: 978-3-0348-7958-3

  • eBook Packages: Springer Book Archive

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