Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data
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| Format: | Preprint |
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2022
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| _version_ | 1866915430843547648 |
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| author | Zou, Jungang Wang, Sijian Chen, Qixuan |
| author_facet | Zou, Jungang Wang, Sijian Chen, Qixuan |
| contents | Multiple imputation is widely used for handling missing data in real-world applications. For variable selection on multiply-imputed datasets, however, if selection is performed on each imputed dataset separately, it can result in different sets of selected variables across datasets. MI-LASSO, one of the most commonly used approaches to this problem, regards the same variable across all separate imputed datasets as a group variable and exploits the group LASSO to yield a consistent variable selection across all the multiply-imputed datasets. In this paper, we extend MI-LASSO to a Bayesian framework and propose four Bayesian MI-LASSO models for variable selection on multiply-imputed data, including three shrinkage prior-based and one Spike-Slab prior-based methods. To further support robust variable selection, we develop a four-step projection predictive variable selection procedure that avoids ad hoc thresholding and facilitates valid post-selection inference. Simulation studies showed that the Bayesian MI-LASSO outperformed MI-LASSO and other alternative approaches, achieving higher specificity and lower mean squared error across a range of settings. We further demonstrated these methods via a case study using a multiply-imputed dataset from the University of Michigan Dioxin Exposure Study. The R package BMIselect is available on CRAN. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2211_00114 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data Zou, Jungang Wang, Sijian Chen, Qixuan Methodology Multiple imputation is widely used for handling missing data in real-world applications. For variable selection on multiply-imputed datasets, however, if selection is performed on each imputed dataset separately, it can result in different sets of selected variables across datasets. MI-LASSO, one of the most commonly used approaches to this problem, regards the same variable across all separate imputed datasets as a group variable and exploits the group LASSO to yield a consistent variable selection across all the multiply-imputed datasets. In this paper, we extend MI-LASSO to a Bayesian framework and propose four Bayesian MI-LASSO models for variable selection on multiply-imputed data, including three shrinkage prior-based and one Spike-Slab prior-based methods. To further support robust variable selection, we develop a four-step projection predictive variable selection procedure that avoids ad hoc thresholding and facilitates valid post-selection inference. Simulation studies showed that the Bayesian MI-LASSO outperformed MI-LASSO and other alternative approaches, achieving higher specificity and lower mean squared error across a range of settings. We further demonstrated these methods via a case study using a multiply-imputed dataset from the University of Michigan Dioxin Exposure Study. The R package BMIselect is available on CRAN. |
| title | Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data |
| topic | Methodology |
| url | https://arxiv.org/abs/2211.00114 |