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A System of Subroutines For Iteratively Reweighted Least Squares Computations

Author

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  • David E. Coleman
  • Paul W. Holland
  • Neil Kaden
  • Virginia Klema

Abstract

A description of a system of subroutines to compute solutions to the iteratively reweighted least squares problem is presented. The weights are determined from the data and linear fit and are computed as functions of the scaled residuals. Iteratively reweighted least squares is a part of robust statistics where "robustness" means relative insensitivity to moderate departures from assumptions. The software for iteratively reweighted least squares is cast as semi-portable Fortran code whose performance is unaffected (in the sense that performance will not be degraded) by the computer or operating-system environment in which it is used. An [ell sub1] start and an [ell sub2] start are provided. Eight weight functions, a numerical rank determination, convergence criterion, and a stem-and-leaf display are included.

Suggested Citation

  • David E. Coleman & Paul W. Holland & Neil Kaden & Virginia Klema, 1977. "A System of Subroutines For Iteratively Reweighted Least Squares Computations," NBER Working Papers 0189, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:0189
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    Cited by:

    1. Bissantz, Nicolai & Dümbgen, Lutz & Munk, Axel & Stratmann, Bernd, 2008. "Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces," Technical Reports 2008,25, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Mastronardi, Nicola & O'Leary, Dianne P., 2007. "Fast robust regression algorithms for problems with Toeplitz structure," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1119-1131, October.

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