Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood

Publikation: Working paper › Forskning

Standard

Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood. / Berenguer-Rico, Vanessa; Johansen, Søren; Nielsen, Bent.

2019.

Publikation: Working paper › Forskning

Harvard

Berenguer-Rico, V, Johansen, S & Nielsen, B 2019 'Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood'. https://doi.org/10.2139/ssrn.3455870

APA

Berenguer-Rico, V., Johansen, S., & Nielsen, B. (2019). Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood. University of Copenhagen. Institute of Economics. Discussion Papers (Online) Nr. 19-11 https://doi.org/10.2139/ssrn.3455870

Vancouver

Berenguer-Rico V, Johansen S, Nielsen B. Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood. 2019 sep. 27. https://doi.org/10.2139/ssrn.3455870

Author

Berenguer-Rico, Vanessa ; Johansen, Søren ; Nielsen, Bent. / Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood. 2019. (University of Copenhagen. Institute of Economics. Discussion Papers (Online); Nr. 19-11).

Bibtex

@techreport{c1ffe610659b4c96bfa6149d9164b77e,
title = "Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood",
abstract = "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 h1/2 consistent and asymptotically standard normal. The LMS estimator is found to be h consistent and asymptotically Laplace. ",
keywords = "Chebychev estimator, LMS, Uniform distribution, Least squares estimator, LTS, Normal distribution, Regression, Robust statistics, Chebychev estimator, LMS, Uniform distribution, Least squares estimator, LTS, Normal distribution, Regression, Robust statistics, C01, C13",
author = "Vanessa Berenguer-Rico and S{\o}ren Johansen and Bent Nielsen",
year = "2019",
month = sep,
day = "27",
doi = "10.2139/ssrn.3455870",
language = "English",
series = "University of Copenhagen. Institute of Economics. Discussion Papers (Online)",
number = "19-11",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood

AU - Berenguer-Rico, Vanessa

AU - Johansen, Søren

AU - Nielsen, Bent

PY - 2019/9/27

Y1 - 2019/9/27

N2 - 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 h1/2 consistent and asymptotically standard normal. The LMS estimator is found to be h consistent and asymptotically Laplace.

AB - 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 h1/2 consistent and asymptotically standard normal. The LMS estimator is found to be h consistent and asymptotically Laplace.

KW - Chebychev estimator

KW - LMS

KW - Uniform distribution

KW - Least squares estimator

KW - LTS

KW - Normal distribution

KW - Regression

KW - Robust statistics

KW - Chebychev estimator

KW - LMS

KW - Uniform distribution

KW - Least squares estimator

KW - LTS

KW - Normal distribution

KW - Regression

KW - Robust statistics

KW - C01

KW - C13

U2 - 10.2139/ssrn.3455870

DO - 10.2139/ssrn.3455870

M3 - Working paper

T3 - University of Copenhagen. Institute of Economics. Discussion Papers (Online)

BT - Models Where the Least Trimmed Squares and Least Median of Squares Estimators Are Maximum Likelihood

ER -

ID: 248551490