Regularized least weighted squares estimator in linear regression
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Regularized least weighted squares estimator in linear regression
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Communications in Statistics - Simulation and Computation
ISSN
0361-0918
e-ISSN
1532-4141
Volume of the periodical
54
Issue of the periodical within the volume
6
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
1890-1900
UT code for WoS article
001138468500001
EID of the result in the Scopus database
2-s2.0-85181656125