Efficient estimates in regression models with highly correlated covariates

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61389005:_____/20:00523766

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient estimates in regression models with highly correlated covariates

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Efficient estimates in regression models with highly correlated covariates

Popis výsledku anglicky

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.

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/GA17-01706S" target="_blank" >GA17-01706S: Matematicko-fyzikální modely nových materiálů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Journal of Computational and Applied Mathematics

ISSN

0377-0427

e-ISSN

—

Svazek periodika

373

Číslo periodika v rámci svazku

1 August 2020

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000521510200016

EID výsledku v databázi Scopus

2-s2.0-85071096796