ArticleCommunications in statistics: Simulation and computation2017
A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures.
Article in Communications in statistics: Simulation and computation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Feature selection and aggregation for antibiotic resistance GWAS inFrontiers in microbiology · 2025Article
- Subsampling based variable selection for generalized linear models.Computational statistics & data analysis · 2023Article
- Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease.Scientific reports · 2023Article
- Identifying Predictors of University Students' Wellbeing during the COVID-19 Pandemic-A Data-Driven Approach.International journal of environmental research and public health · 2021Article
- A comparison of model selection methods for prediction in the presence of multiply imputed data.Biometrical journal. Biometrische Zeitschrift · 2019Article
- Factors associated with common mental health problems of humanitarian workers in South Sudan.PloS one · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Classification models can demonstrate apparent prediction accuracy even when there is no underlying relationship between the predictors and the response. Variable selection procedures can lead to false positive variable selections and overestimation of true model performance. A simulation study was conducted using logistic regression with forward stepwise, best subsets, and LASSO variable selection methods with varying total sample sizes (20, 50, 100, 200) and numbers of random noise predictor variables (3, 5, 10, 15, 20, 50). Using our critical values can help reduce needless follow-up on variables having no true association with the outcome.
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