ArticleBiometrics2015
A permutation approach for selecting the penalty parameter in penalized model selection.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- A Simple Information Criterion for Variable Selection in High-Dimensional Regression.Statistics in medicine · 2025Article
- Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data.Communications biology · 2024Article
- Variable selection for discrete survival model with frailty in presence of left truncation and right censoring: Studying association of environmental toxicants on time-to-pregnancy.Statistics in medicine · 2023Article
- Development and validation of a risk score to predict the frequent emergency house calls among older people who receive regular home visits.BMC primary care · 2022Article
- Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models.Lifetime data analysis · 2022Article
- New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection.BMC medical research methodology · 2021Article
- Predictors of Successful Yttrium-90 Radioembolization Bridging or Downstaging in Patients with Hepatocellular Carcinoma.Canadian journal of gastroenterology & hepatology · 2021Article
- I-Boost: an integrative boosting approach for predicting survival time with multiple genomics platforms.Genome biology · 2019Article
- Prenatal Phthalate, Perfluoroalkyl Acid, and Organochlorine Exposures and Term Birth Weight in Three Birth Cohorts: Multi-Pollutant Models Based on Elastic Net Regression.Environmental health perspectives · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We describe a simple, computationally efficient, permutation-based procedure for selecting the penalty parameter in LASSO-penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), scaled sparse linear regression, and a selection method based on recently developed testing procedures for the LASSO.
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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.