Variable selection methods for descriptive modeling Article Swipe
Anuja Dharmaratne
,
Alysha De Livera
,
S. Georgiou
,
Stella Stylianou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0321601
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0321601
Variable selection methods are widely used in observational studies. While many penalty-based statistical methods introduced in recent decades have primarily focused on prediction, classical statistical methods remain the standard approach in applied research and education. In this study, we evaluated the variable selection performance of several widely used classical and modern methods for descriptive modeling, using both simulated and real data. A novel aspect of our research is the incorporation of a statistical approach inspired by the supersaturated design-based factor screening method in an observational setting. The methods were evaluated based on Type I and Type II error rates, the average number of predictors selected, variable inclusion frequency, absolute bias, and root mean square error. The detailed results of these evaluations are presented, and the methods’ performance is discussed across various simulation scenarios and in application to real data.
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- Language
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- Landing Page
- https://doi.org/10.1371/journal.pone.0321601
- OA Status
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- 92
- Related Works
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- OpenAlex ID
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https://doi.org/10.1371/journal.pone.0321601Digital Object Identifier
- Title
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Variable selection methods for descriptive modelingWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-06-02Full publication date if available
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Anuja Dharmaratne, Alysha De Livera, S. Georgiou, Stella StylianouList of authors in order
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https://doi.org/10.1371/journal.pone.0321601Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1371/journal.pone.0321601Direct OA link when available
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Observational study, Computer science, Mean squared error, Variable (mathematics), Statistics, Selection (genetic algorithm), Type I and type II errors, Feature selection, Data mining, Machine learning, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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