Original paper
EMLasso: logistic lasso with missing data
Abstract
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 model selection. In earlier work, we...
Paper Details
Title
EMLasso: logistic lasso with missing data
Published Date
Feb 25, 2013
Journal
Volume
32
Issue
18
Pages
3143 - 3157
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Notes
History