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Article

EMLasso: logistic lasso with missing data.

Statistics in medicine - 15 Aug 2013

Sabbe N, Thas O, Ottoy J-P

Abstract excerpt

In clinical settings, missing data in the covariates occur frequently. For example, some markers are expensive or hard to measure. When this sort of data is used for model selection, the missingness is often resolved through a complete case analysis or a form of single imputation. An alternative sometimes comes in the form of leaving the most damaged covariates out. All these strategies jeopardise the goal of...

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PMID: 23440969
DOI: 10.1002/sim.5760

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