Statistical analysis with missing data
Statistical analysis with missing data
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
Finding temporal patterns in noisy longitudinal data: a study in diabetic retinopathy
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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The problem of missing values commonly arises in data sets, and imputation is usually employed to compensate for non-response. We propose a novel imputation method based on quantiles, which can be implemented with or without the presence of auxiliary information. The proposed method is extended to unequal sampling designs and non-uniform response mechanisms. Iterative algorithms to compute the proposed imputation methods are presented. Monte Carlo simulations are conducted to assess the performance of the proposed imputation methods with respect to alternative imputation methods. Simulation results indicate that the proposed methods perform competitively in terms of relative bias and relative root mean square error.