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Imputation by Gaussian Copula Model with an Application to Incomplete Customer Satisfaction Data

In: Proceedings of COMPSTAT'2010

Author

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  • Meelis Käärik

    (University of Tartu, Institute of Mathematical Statistics)

  • Ene Käärik

    (University of Tartu, Institute of Mathematical Statistics)

Abstract

We propose the idea of imputing missing value based on conditional distributions, which requires the knowledge of the joint distribution of all the data. The Gaussian copula is used to find a joint distribution and to implement the conditional distribution approach. The focus remains on the examination of the appropriateness of an imputation algorithm based on the Gaussian copula. In the present paper, we generalize and apply the copula model to incomplete correlated data using the imputation algorithm given by Käärik and Käärik (2009a). The empirical context in the current paper is an imputation model using incomplete customer satisfaction data. The results indicate that the proposed algorithm performs well.

Suggested Citation

  • Meelis Käärik & Ene Käärik, 2010. "Imputation by Gaussian Copula Model with an Application to Incomplete Customer Satisfaction Data," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 485-492, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_48
    DOI: 10.1007/978-3-7908-2604-3_48
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