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Imputation for Skewed Data: Multivariate Lomax Case

Zhixin Lun () and Ravindra Khattree ()
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Zhixin Lun: Oakland University
Ravindra Khattree: Oakland University

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 6, 86-113

Abstract: Abstract Most multiple imputation methods for multivariate missing data have been developed for normally distributed data. However, methods may not be suitable for nonnegative and/or highly skewed data. We propose an approach by using Expectation-Maximization (EM) method based on the assumption of multivariate Lomax distribution on non-negative skewed data. Extensive simulations show that this proposed method outperforms the regular normality-based EM and k-nearest-neighbor (k NN) imputation methods under the missing completely at random (MCAR) mechanism. An application on a real-world biomedical data is then provided.

Keywords: Copula transformation; EM method; imputation; k-nearest-neighbor; missing data; multivariate Lomax distribution; Skewed data.; Primary 62H99; Secondary 62M20 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13571-021-00251-4

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Handle: RePEc:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-021-00251-4