eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2025-04-08 13 2 24 29 10.12691/ajams-13-2-1 AJAMS20251321 article Zanhua Yin yinzh226@163.com 1 Zhichao Wang 1 School of Mathematical and Computer Sciences, Gannan Normal University, Ganzhou, People¡¯s Republic of China 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. https://pubs.sciepub.com/ajams/13/2/1/ajams-13-2-1.pdf Corrected score Lasso logistic regression model measurement error sparse