Models where the Least Trimmed Squares and Least Median of Squares estimators are maximum likelihood

Publikation: Working paperForskning

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  • rp19_15

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  • Vanessa Berenguer-Rico, University of Oxford, Oxford, Storbritannien
  • Søren Johansen
  • Bent Nielsen, University of Oxford
The Least Trimmed Squares (LTS) and Least Median of Squares (LMS) estimators are popular robust regression estimators. The idea behind the estimators is to find, for a given h, a sub-sample of h `good' observations among n observations and estimate the regression on that sub-sample. We find models, based on the normal or the uniform distribution respectively, in which these estimators are maximum likelihood. We provide an asymptotic theory for the location-scale case in those models. The LTS estimator is found to be sqrt(h) consistent and asymptotically standard normal. The LMS estimator is found to be h consistent and asymptotically Laplace.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider41
StatusUdgivet - 19 sep. 2019
SerietitelCREATES Research Papers
Nummer2019/15

    Forskningsområder

  • Chebychev estimator, LMS, Uniform distribution, Least squares estimator, LTS, Normal distribution, Regression, Robust statistics

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