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

Meelis Käärik () and Ene Käärik ()
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Meelis Käärik: University of Tartu, Institute of Mathematical Statistics
Ene Käärik: University of Tartu, Institute of Mathematical Statistics

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 485-492 from Springer

Abstract: 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.

Keywords: Gaussian copula; incomplete data; imputation (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_48

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DOI: 10.1007/978-3-7908-2604-3_48

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