Regularized least weighted squares estimator in linear regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00604122" target="_blank" >RIV/67985807:_____/25:00604122 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1080/03610918.2023.2300356" target="_blank" >https://doi.org/10.1080/03610918.2023.2300356</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03610918.2023.2300356" target="_blank" >10.1080/03610918.2023.2300356</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regularized least weighted squares estimator in linear regression

Popis výsledku v původním jazyce

This article is interested in estimating parameters of the linear regression model in a high-dimensional setting, i.e. with a large number of regressors. The lasso estimator does not possess high robustness with respect to the presence of outliers in the data. Our approach extends the least weighted squares estimator, which has appealing robustness and efficiency properties in linear regression with a small number of regressors. The novel LWS-lasso estimator is proposed here as an L1-regularized version of the least weighted squares. The analysis of a world tourism dataset as well as simulations show that LWS-lasso may outperform available regression estimators, especially in scenarios with high-dimensional data with a higher contamination by outliers.

Název v anglickém jazyce

Regularized least weighted squares estimator in linear regression

Popis výsledku anglicky

This article is interested in estimating parameters of the linear regression model in a high-dimensional setting, i.e. with a large number of regressors. The lasso estimator does not possess high robustness with respect to the presence of outliers in the data. Our approach extends the least weighted squares estimator, which has appealing robustness and efficiency properties in linear regression with a small number of regressors. The novel LWS-lasso estimator is proposed here as an L1-regularized version of the least weighted squares. The analysis of a world tourism dataset as well as simulations show that LWS-lasso may outperform available regression estimators, especially in scenarios with high-dimensional data with a higher contamination by outliers.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Aproximativní neurovýpočty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Communications in Statistics - Simulation and Computation

ISSN

0361-0918

e-ISSN

1532-4141

Svazek periodika

54

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

1890-1900

Kód UT WoS článku

001138468500001

EID výsledku v databázi Scopus

2-s2.0-85181656125