ArticleStatistics in medicine2020
Variable selection for high-dimensional partly linear additive Cox model with application to Alzheimer's disease.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data.Journal of statistical planning and inference · 2026Article
- Broken adaptive ridge method for variable selection in generalized partly linear models with application to the coronary artery disease data.Journal of Computational Mathematics and Data Science · 2025Article
- Post-selection inference for the Cox model with interval-censored data.Scandinavian journal of statistics, theory and applications · 2025Article
- Partly linear single-index cure models with a nonparametric incidence link function.Statistical methods in medical research · 2024Article
- Neural network on interval-censored data with application to the prediction of Alzheimer's disease.Biometrics · 2023Article
- Bayesian analysis for partly linear Cox model with measurement error and time-varying covariate effect.Statistics in medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Variable selection has been discussed under many contexts and especially, a large literature has been established for the analysis of right-censored failure time data. In this article, we discuss an interval-censored failure time situation where there exist two sets of covariates with one being low-dimensional and having possible nonlinear effects and the other being high-dimensional. For the problem, we present a penalized estimation procedure for simultaneous variable selection and estimation, and in the method, Bernstein polynomials are used to approximate the involved nonlinear functions. Furthermore, for implementation, a coordinate-wise optimization algorithm, which can accommodate most commonly used penalty functions, is developed. A numerical study is performed for the evaluation of the proposed approach and suggests that it works well in practical situations. Finally the method is applied to an Alzheimer's disease study that motivated this investigation.
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Registered trials
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.