An algorithm for deepest multiple regression View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2000

AUTHORS

Peter J. Rousseeuw , Stefan Van Aelst

ABSTRACT

Deepest regression (DR) is a method for linear regression introduced by Rousseeuw and Hubert (1999). The DR is defined as the fit with largest regression depth relative to the data. DR is a robust regression method. We construct an approximate algorithm for fast computation of DR in more than two dimensions. We also construct simultaneous confidence regions for the true unknown parameters, based on bootstrapped estimates. More... »

PAGES

139-150

References to SciGraph publications

  • 1998-08. Computing location depth and regression depth in higher dimensions in STATISTICS AND COMPUTING
  • 1985. Multivariate Estimation with High Breakdown Point in MATHEMATICAL STATISTICS AND APPLICATIONS
  • 1984. Robust Regression by Means of S-Estimators in ROBUST AND NONLINEAR TIME SERIES ANALYSIS
  • Book

    TITLE

    COMPSTAT

    ISBN

    978-3-7908-1326-5
    978-3-642-57678-2

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-57678-2_13

    DOI

    http://dx.doi.org/10.1007/978-3-642-57678-2_13

    DIMENSIONS

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