ArticleJournal of the American Statistical Association2015
The E-MS Algorithm: Model Selection with Incomplete Data.
Article in Journal of the American Statistical Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 30 citations in OpenAlex.
- How to apply Bayesian stochastic search variable selection with multiply imputed data.Psychological methods · 2026Article
- Bayesian semiparametric inference in longitudinal metabolomics data.Scientific reports · 2024Article
- Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology.Psychometrika · 2023Review
- PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA.Statistica Sinica · 2023Article
- Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang
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