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Updated: Sep 8, 2025
07:35
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
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Best-subset model selection based on multitudinal assessments of likelihood improvements
Knute D Carter1, Joseph E Cavanaugh1
1Department of Biostatistics, University of Iowa, Iowa City, IA, USA.
Journal of Applied Statistics
|June 16, 2022
Summary
The SIFT procedure offers a new approach to statistical model selection by controlling the probability of including spurious variables. This method helps identify the true data generating mechanism more reliably.
Area of Science:
- Statistics
- Computational Statistics
Background:
- Model selection involves identifying the best model from subsets of explanatory variables, which is computationally and statistically challenging.
- Existing methods struggle to control the selection of spurious variables.
Purpose of the Study:
- To introduce a novel model selection procedure, the SIFT (Selecting Improved Fitting Terms) procedure.
- To provide researchers with control over the probability of selecting models with spurious variables.
Main Methods:
- The SIFT procedure evaluates the contribution of each candidate variable to model fitting.
- Two variants are proposed: a naive method and an empirical permutation-based method.
- Methods are analyzed within the linear modeling framework and compared to existing techniques.
Main Results:
- The SIFT procedure effectively selects variables that represent the true data generating mechanism.
- It successfully limits the selection of spurious variables to a user-defined probability level.
- Performance is validated against other established model selection techniques.
Conclusions:
- The SIFT methodology provides a robust and controllable approach to statistical model selection.
- It empowers researchers to manage the risk of including irrelevant variables based on application-specific needs.
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