ReviewJournal of applied statistics2021
An elastic-net penalized expectile regression with applications.
Review in Journal of applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.Oncology letters · 2026Article
- Identification and Experimental Validation of Key Biomarkers for Rheumatoid Arthritis Based on Bioinformatics Analysis and Machine Learning.Journal of inflammation research · 2026Article
- A Cross-Tissue Multiomics Analysis Reveals the Protective Role of TGFBR3 in Postmenopausal Osteoporosis.International journal of genomics · 2026Article
- Development and Internal Validation of an Explainable Machine Learning Model for Physical Activity Adherence Among Community-Dwelling Patients with Type 2 Diabetes Mellitus.Patient preference and adherence · 2026Article
- Development and Validation of an Electronic Health Record-Based Algorithm for Identifying Patients With Long-Term Opioid Therapy: Cross-Sectional Study.Journal of medical Internet research · 2025Article
- Article
- The interaction effect of metals exposure and dietary habit on cognitive function in Chinese older adult cohort.The journal of nutrition, health & aging · 2024Article
- Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke.Scientific reports · 2023Article
- A new GEE method to account for heteroscedasticity using asymmetric least-square regressions.Journal of applied statistics · 2022Article
- Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.