A model where the least trimmed squares estimator is maximum likelihood

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Standard

A model where the least trimmed squares estimator is maximum likelihood. / Berenguer-Rico, Vanessa; Johansen, Soren; Nielsen, Bent.

I: Journal of The Royal Statistical Society Series B-statistical Methodology, Bind 85, Nr. 3, 2023, s. 886-912.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Berenguer-Rico, V, Johansen, S & Nielsen, B 2023, 'A model where the least trimmed squares estimator is maximum likelihood', Journal of The Royal Statistical Society Series B-statistical Methodology, bind 85, nr. 3, s. 886-912. https://doi.org/10.1093/jrsssb/qkad028

APA

Berenguer-Rico, V., Johansen, S., & Nielsen, B. (2023). A model where the least trimmed squares estimator is maximum likelihood. Journal of The Royal Statistical Society Series B-statistical Methodology, 85(3), 886-912. https://doi.org/10.1093/jrsssb/qkad028

Vancouver

Berenguer-Rico V, Johansen S, Nielsen B. A model where the least trimmed squares estimator is maximum likelihood. Journal of The Royal Statistical Society Series B-statistical Methodology. 2023;85(3):886-912. https://doi.org/10.1093/jrsssb/qkad028

Author

Berenguer-Rico, Vanessa ; Johansen, Soren ; Nielsen, Bent. / A model where the least trimmed squares estimator is maximum likelihood. I: Journal of The Royal Statistical Society Series B-statistical Methodology. 2023 ; Bind 85, Nr. 3. s. 886-912.

Bibtex

@article{646c09b2ba604e81b66ec9ca8d469b25,
title = "A model where the least trimmed squares estimator is maximum likelihood",
abstract = "The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of h {\textquoteleft}good{\textquoteright} observations among n observations and applies least squares on that subsample. We formulate a model in which this estimator is maximum likelihood. The model has {\textquoteleft}outliers{\textquoteright} of a new type, where the outlying observations are drawn from a distribution with values outside the realized range of h {\textquoteleft}good{\textquoteright}, normal observations. The LTS estimator is found to be h 1/2 consistent and asymptotically standard normal in the location-scale case. Consistent estimation of h is discussed. The model differs from the commonly used E-contamination models and opens the door for statistical discussion on contamination schemes, new methodological developments on tests for contamination as well as inferences based on the estimated good data.",
keywords = "leverage, least median of squares estimator, outliers, regression, robust statistics",
author = "Vanessa Berenguer-Rico and Soren Johansen and Bent Nielsen",
year = "2023",
doi = "10.1093/jrsssb/qkad028",
language = "English",
volume = "85",
pages = "886--912",
journal = "Journal of the Royal Statistical Society, Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley",
number = "3",

}

RIS

TY - JOUR

T1 - A model where the least trimmed squares estimator is maximum likelihood

AU - Berenguer-Rico, Vanessa

AU - Johansen, Soren

AU - Nielsen, Bent

PY - 2023

Y1 - 2023

N2 - The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of h ‘good’ observations among n observations and applies least squares on that subsample. We formulate a model in which this estimator is maximum likelihood. The model has ‘outliers’ of a new type, where the outlying observations are drawn from a distribution with values outside the realized range of h ‘good’, normal observations. The LTS estimator is found to be h 1/2 consistent and asymptotically standard normal in the location-scale case. Consistent estimation of h is discussed. The model differs from the commonly used E-contamination models and opens the door for statistical discussion on contamination schemes, new methodological developments on tests for contamination as well as inferences based on the estimated good data.

AB - The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of h ‘good’ observations among n observations and applies least squares on that subsample. We formulate a model in which this estimator is maximum likelihood. The model has ‘outliers’ of a new type, where the outlying observations are drawn from a distribution with values outside the realized range of h ‘good’, normal observations. The LTS estimator is found to be h 1/2 consistent and asymptotically standard normal in the location-scale case. Consistent estimation of h is discussed. The model differs from the commonly used E-contamination models and opens the door for statistical discussion on contamination schemes, new methodological developments on tests for contamination as well as inferences based on the estimated good data.

KW - leverage

KW - least median of squares estimator

KW - outliers

KW - regression

KW - robust statistics

U2 - 10.1093/jrsssb/qkad028

DO - 10.1093/jrsssb/qkad028

M3 - Journal article

VL - 85

SP - 886

EP - 912

JO - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society, Series B (Statistical Methodology)

SN - 1369-7412

IS - 3

ER -

ID: 355222550