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Activity Number:
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601
- Recent Advances in Variable Selection for Linear and Nonlinear Models
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #304920
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Presentation
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Title:
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Optimized Variable Selection via Repeated Data Splitting
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Author(s):
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Marinela Capanu* and Colin Begg and Mithat Gonen
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Companies:
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Memorial Sloan Kettering Cancer Center and Memorial Sloan Kettering Cancer Center and Memorial Sloan Kettering Cancer Center
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Keywords:
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variable selection;
linear regression;
data splitting;
empirical threshold;
variable screening
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Abstract:
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We introduce a new variable selection procedure that repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final regression model. In an extensive simulation study we show that the proposed variable selection technique enjoys superior performance compared to candidate methods, being amongst those with the lowest inclusion of noisy predictors while having the highest power to detect the correct model and being unaffected by correlations among the predictors. We illustrate the methods by applying them to a cohort of patients undergoing hepatectomy at our institution.
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Authors who are presenting talks have a * after their name.
