Johannes Bleher, Claudia Tarantola
arXiv 14 Apr 2026 · Statistics — Methodology
arXiv:2604.12783 · PDF · DOI · OpenAlex · Extracted main text
When variable selection methods are applied to bootstrapped and multiply imputed datasets, the set of selected variables typically varies across iterations. Aggregating results via the union rule can lead to overly dense models. We propose a sequential evidence aggregation procedure that models detection outcomes across perturbation iterations as Bernoulli trials and accumulates evidence for variable relevance through a likelihood-ratio process admitting an approximate Bayes-factor interpretation. The procedure provides both a variable inclusion criterion and a stopping rule that eliminates the need to fix the number of bootstrap-imputation iterations ex ante. A Monte Carlo study across 126 scenarios and an empirical illustration demonstrate the method's performance relative to existing aggregation approaches.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Belloni, Alexandre and Chernozhukov, Victor and Hansen, Christian (2014) Inference on Treatment Effects after Selection among High-Dimensional Controls | 0.928 | 4 | 4 | 100% |
| 2 | Belloni, Alexandre and Chernozhukov, Victor and Hansen, Christian (2014) High-Dimensional Methods and Inference on Structural and Treatment Effects | 0.928 | 4 | 3 | 100% |
| 3 | Rubin, Donald B (1987) Multiple Imputation for Nonresponse in Surveys | 0.843 | 3 | 3 | 100% |
| 4 | Bainter, Sierra A. and McCauley, Thomas G. and Fahmy, Mahmoud Maher… (2023) Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology | 0.405 | 1 | 1 | 100% |
| 5 | Chen, Qixuan and Wang, Sijian (2013) Variable selection for multiply-imputed data with application to dioxin exposure study | 0.405 | 1 | 1 | 100% |
| 6 | Du, Jiacong and Boss, Jonathan and Han, Peisong and Beesley, Lauren… (2020) Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods | 0.405 | 1 | 1 | 100% |
| 7 | Edwards, Ward and Lindman, Harold R. and Savage, Leonard J (1963) Bayesian statistical inference for psychological research | 0.405 | 1 | 1 | 100% |
| 8 | Efron, Bradley (1979) Bootstrap Methods: Another Look at the Jackknife | 0.405 | 1 | 1 | 100% |
| 9 | Efron, Bradley and Tibshirani, Robert J (1994) An Introduction to the Bootstrap | 0.405 | 1 | 1 | 100% |
| 10 | George, Edward I. and McCulloch, Robert E (1997) Approaches for Bayesian variable selection | 0.405 | 1 | 1 | 100% |
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