Extending Lawson's Algorithm to Include the Huber M-Estimator

Abstract

When fitting a curve to experimental data, there is no guarantee that the data obtained are as accurate as might be expected. The effect of outside influences may cause the data set to contain outliers. These outliers can have a significant effect on any curve which is fitted to such data. The l infinity-norm, which is particularly appropriate for fitting data with uniformly distributed errors, is extremely sensitive to such outliers, since it minimises the maximum error from the data to the curve. Therefore, a technique which approximates a data set using the l infinity-norm, without being adversely affected by outliers, would be a useful addition to the array of tools available. We present numerical examples to illustrate the use of such a technique and also some practical applications to justify its use.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2000
Accession Number
ADP011967

Entities

People

  • Colin Ross
  • Iain J. Anderson
  • John C. Mason

Organizations

  • University of Huddersfield

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Convergence
  • Data Sets
  • Errors
  • Estimators
  • Experimental Data
  • Iterations
  • Measurement
  • Polynomials
  • Probability
  • Probability Distributions
  • Residuals
  • Statistical Algorithms
  • Technical Information Centers

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.