2026-08-16T09:08:10Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/72492023-08-26T01:49:22Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Basu, A. author Ghosh, A. author Jaenada Malagón, María author Pardo Llorente, Leandro 2023-06-17T08:28:55Z 2023-06-17T08:28:55Z 2021 https://hdl.handle.net/20.500.14352/7249 Penalized logistic regression is extremely useful for binary classiffication with a large number of covariates (significantly higher than the sample size), having several real life applications, including genomic disease classification. However, the existing methods based on the likelihood based loss function are sensitive to data contamination and other noise and, hence, robust methods are needed for stable and more accurate inference. In this paper, we propose a family of robust estimators for sparse logistic models utilizing the popular density power divergence based loss function and the general adaptively weighted LASSO penalties. We study the local robustness of the proposed estimators through its in uence function and also derive its oracle properties and asymptotic distribution. With extensive empirical illustrations, we clearly demonstrate the significantly improved performance of our proposed estimators over the existing ones with particular gain in robustness. Our proposal is finally applied to analyse four different real datasets for cancer classification, obtaining robust and accurate models, that simultaneously performs gene selection and patient classification. eng Robust adaptive Lasso in high-dimensional logistic regression with an application to genomic classification of cancer patients journal article URL https://docta.ucm.es/bitstreams/ee270d2d-3359-42ef-878d-dc1acfe2ca39/download File MD5 9d0a4332cf64013e59d9e6dc9d3fe741 490344 application/pdf pardoleandro_robustadaptiveLasso.pdf