The oracle property of the generalized outcome adaptive lasso

Fuente: arXiv
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Auteur principal: Baldé, Ismaila
Format: Preprint
Publié: 2023
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author Baldé, Ismaila
author_facet Baldé, Ismaila
contents The generalized outcome-adaptive lasso (GOAL) is a variable selection for high-dimensional causal inference proposed by Baldé et al. [2023, {\em Biometrics} {\bfseries 79(1)}, 514--520]. When the dimension is high, it is now well established that an ideal variable selection method should have the oracle property to ensure the optimal large sample performance. However, the oracle property of GOAL has not been proven. In this paper, we show that the GOAL estimator enjoys the oracle property. Our simulation shows that the GOAL method deals with the collinearity problem better than the oracle-like method, the outcome-adaptive lasso (OAL).
format Preprint
id arxiv_https___arxiv_org_abs_2310_00250
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle The oracle property of the generalized outcome adaptive lasso
Baldé, Ismaila
Statistics Theory
Methodology
The generalized outcome-adaptive lasso (GOAL) is a variable selection for high-dimensional causal inference proposed by Baldé et al. [2023, {\em Biometrics} {\bfseries 79(1)}, 514--520]. When the dimension is high, it is now well established that an ideal variable selection method should have the oracle property to ensure the optimal large sample performance. However, the oracle property of GOAL has not been proven. In this paper, we show that the GOAL estimator enjoys the oracle property. Our simulation shows that the GOAL method deals with the collinearity problem better than the oracle-like method, the outcome-adaptive lasso (OAL).
title The oracle property of the generalized outcome adaptive lasso
topic Statistics Theory
Methodology
url https://arxiv.org/abs/2310.00250