Numerical methods for estimating the tuning parameter in penalized least squares problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AA2302EC6" target="_blank" >RIV/61988987:17310/22:A2302EC6 - isvavai.cz</a>
Alternative codes found
RIV/61988987:17310/19:A20023EA
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/03610918.2019.1676436" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/03610918.2019.1676436</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03610918.2019.1676436" target="_blank" >10.1080/03610918.2019.1676436</a>
Alternative languages
Result language
angličtina
Original language name
Numerical methods for estimating the tuning parameter in penalized least squares problems
Original language description
The solution of the penalized least squares problems depends on a tuning parameter. A popular tool for specifying the tuning parameter is the generalized cross-validation (GCV). In this work, we utilize estimates for the GCV function whose minimizers can lead to the determination of the tuning parameter. The selection of an efficient estimate depends on an appropriately defined index of proximity. Bounds and specific values are derived for this index and a thorough study proves that the proposed one-term estimate suits perfectly to statistical models with high correlated variables. This is confirmed through simulation tests for several datasets.
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
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
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
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Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
1542-1563
UT code for WoS article
000491387100001
EID of the result in the Scopus database
2-s2.0-85074442563