Linear predictor on linearly-generated data with missing values: non consistency and solutions
Abstract
We consider building predictors when the data have missing values. We study the seemingly-simple case where the target to predict is a linear function of the fully-observed data and we show that, in the presence of missing values, the optimal predictor may not be linear. In the particular Gaussian case, it can be written as a linear function of multiway interactions between the observed data and the various missing-value indicators. Due to its...
Paper Details
Title
Linear predictor on linearly-generated data with missing values: non consistency and solutions
Published Date
Aug 26, 2020
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