Efficient estimates in regression models with highly correlated covariates
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F20%3AA21023E8" target="_blank" >RIV/61988987:17310/20:A21023E8 - isvavai.cz</a>
Alternative codes found
RIV/61389005:_____/20:00523766
Result on the web
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0377042719304194" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0377042719304194</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cam.2019.112416" target="_blank" >10.1016/j.cam.2019.112416</a>
Alternative languages
Result language
angličtina
Original language name
Efficient estimates in regression models with highly correlated covariates
Original language description
The specification of accurate ridge estimates in penalized regression models strongly depends on the appropriate choice of the tuning parameter which monitors the regularization process. In this work, we propose the selection of this parameter via the minimization of an extrapolation estimate of the generalized cross-validation function. The efficiency of the estimate is characterized by an appropriately defined index of proximity; in case that its value approaches one, the estimation becomes optimal. We consider regression models with highly correlated covariates and prove that the probability of the index of proximity being close to one is high. This result is confirmed through several simulation tests.
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/GA17-01706S" target="_blank" >GA17-01706S: Mathematical-Physics Models of Novel Materials</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Computational and Applied Mathematics
ISSN
0377-0427
e-ISSN
—
Volume of the periodical
373
Issue of the periodical within the volume
1 August 2020
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
12
Pages from-to
1-12
UT code for WoS article
000521510200016
EID of the result in the Scopus database
2-s2.0-85071096796