Online Program

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Friday, October 20
Fri, Oct 20, 2:30 PM - 4:00 PM
Aventine Ballroom A
Pushing Technical Boundaries

'Efron's Rules' for Inference After Imputation and Model Selection (303949)

Lin Liu, UCSD Biostatistics 
*Karen Messer, University of California, San Diego 
Loki Natarajan, UC San Diego 

Keywords: missing data, multiple imputation, post-selection inference

We address the practical problem of model selection in the presence of imputation for missing data. Our focus is on valid inference, in particular on confidence intervals that incorporate both the imputation mechanism and the model selection mechanism. We investigate commonly used resampling-based approaches - multiple imputation and the bootstrap - and incorporate Efron's 2014 computationally efficient variance estimate for bootstrap-smoothed estimates. We compare the resulting `Efron's rules' estimator to a 'Rubin's rules' estimator based on multiple imputation. These turn out to be versions of frequentist model averaged estimators, and are compared to an un-averaged selection estimator using the framework of Claeskens and Hjort. Simulation and real data examples are drawn from the related literature. Practical recommendations are given, including circumstances where the new Efron's rules estimator is seen to work well.