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Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data.
Sample selection bias as a specification error
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Good methods for coping with missing data in decision trees
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
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Emmanuel J Candes and Yaniv Plan · 2010
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Rahul Mazumder, Trevor Hastie, and Robert Tibshirani · 2010
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Julie Josse and François Husson · 2012
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Statistical analysis with missing data , volume 333
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softImpute: Matrix Completion via Iterative Soft-Thresholded SVD , 2015
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Regularised pca to denoise and visualise data
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Nice latent variable models have log-rank
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