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Missing Data Imputation Using the Multivariate t Distribution

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  • Liu, C.
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    Abstract

    When a rectangular multivariate data set contains missing values, missing data imputation using the multivariate t distribution appears potentially useful, especially for robust inferences. An efficient technique, called the monotone data augmentation algorithm, for implementing missing data imputation using the multivariate t distribution with known and unknown weights, with monotone and nonmonotone missing data, and with known and unknown degrees of freedom is presented. Two numerical examples are included to illustrate the methodology, to compare results obtained using the multivariate t distribution with results obtained using the normal distribution, and to compare the rate of convergence of the monotone data augmentation algorithm with the rate of convergence of the (rectangular) data augmentation algorithm.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 53 (1995)
    Issue (Month): 1 (April)
    Pages: 139-158

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    Handle: RePEc:eee:jmvana:v:53:y:1995:i:1:p:139-158

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    Cited by:
    1. Jie Jiang & Xinsheng Liu & Keming Yu, 2013. "Maximum likelihood estimation of multinomial probit factor analysis models for multivariate t-distribution," Computational Statistics, Springer, vol. 28(4), pages 1485-1500, August.
    2. Margaret Y Hwang & David Weil, 1997. "The Diffusion of Modern Manufacturing Practices: Evidence from Retail-Apparel Sectors," Working Papers 97-11, Center for Economic Studies, U.S. Census Bureau.
    3. Atkinson, A. C. & Cheng, Tsung-Chi, 2000. "On robust linear regression with incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 33(4), pages 361-380, June.
    4. Sik-Yum Lee & Ye-Mao Xia, 2008. "A Robust Bayesian Approach for Structural Equation Models with Missing Data," Psychometrika, Springer, vol. 73(3), pages 343-364, September.

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