ArticlePloS one2016
An Adaptive Ridge Procedure for L0 Regularization.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Rare variant effect estimation and polygenic risk prediction.Nature genetics · 2026Article
- A Commentary on Chatterjee Et Al. (2018): A Corrected Framework for Group Sparsity in Zero-Inflated Negative Binomial Models.Statistics in medicine · 2025Article
- Sparse kernel sufficient dimension reduction.Journal of nonparametric statistics · 2024Article
- Regression modelling of interval censored data based on the adaptive ridge procedure.Journal of applied statistics · 2022Article
- Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator.Entropy (Basel, Switzerland) · 2021Article
- Sparse Poisson regression via mixed-integer optimization.PloS one · 2021Article
- Variable Selection in Threshold Regression Model with Applications to HIV Drug Adherence Data.Statistics in biosciences · 2020Article
- A surrogate ℓStatistics in medicine · 2020Article
- Extreme learning machine Cox model for high-dimensional survival analysis.Statistics in medicine · 2019Article
- Broken adaptive ridge regression and its asymptotic properties.Journal of multivariate analysis · 2018Article
- Variable selection for recurrent event data with broken adaptive ridge regression.The Canadian journal of statistics = Revue canadienne de statistique · 2018Article
- SAFlex: A structural alphabet extension to integrate protein structural flexibility and missing data information.PloS one · 2018Article
- Complex polymorphisms in endocytosis genes suggest alpha-cyclodextrin as a treatment for breast cancer.PloS one · 2018Article
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Authors and funding
2 authors.
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Abstract
Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. In this paper we introduce an adaptive ridge procedure (AR), where iteratively weighted ridge problems are solved whose weights are updated in such a way that the procedure converges towards selection with L0 penalties. After introducing AR its specific shrinkage properties are studied in the particular case of orthogonal linear regression. Based on extensive simulations for the non-orthogonal case as well as for Poisson regression the performance of AR is studied and compared with SCAD and adaptive LASSO. Furthermore an efficient implementation of AR in the context of least-squares segmentation is presented. The paper ends with an illustrative example of applying AR to analyze GWAS data.
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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.