Adaptive predictor‐set linear model: An imputation‐free method for linear regression prediction on data sets with missing values
Benjamin Planterose Jiménez,
Manfred Kayser,
Athina Vidaki
et al.
Abstract:Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this shortcoming is usually resolved by complete‐case analysis or imputation. Both work‐arounds, however, are inadequate for prediction, since they either fail to predict on incomplete records or ignore missingness‐induced reduction in prediction accuracy and rely on (unr… Show more
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