@article{ajams20251321,
author={{Yin, Zanhua and Wang, Zhichao},
title={Variable Selection for Sparse Logistic Regression Model with Errors in Covariates},
journal={American Journal of Applied Mathematics and Statistics},
volume={13},
number={2},
pages={24--29},
year={2025},
url={https://pubs.sciepub.com/ajams/13/2/1},
issn={2328-7292},
abstract={This paper addresses variable selection problems in sparse logistic regression model with errors-in-covariates. We propose a corrected score Lasso method, which combines the weighted score Lasso approach with a projected gradient descent algorithm, to handle the challenges posed by measurement errors. The weighted score Lasso introduces a correction-amenable score function, enabling direct extension to measurement error scenarios through subsequent score correction. Our method bridges the gap between rigorous measurement error correction and practical high-dimensional implementation, establishing a framework extensible to other generalized linear models with exponential family structure. Numerical studies demonstrate the superior performance of the corrected score Lasso in error correction scenarios, highlighting its potential as a robust tool for high-dimensional data analysis with measurement error.},
doi={10.12691/ajams-13-2-1}
publisher={Science and Education Publishing}
}