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.
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References
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Acknowledgements
This work is supported by Estonian Science Foundation grants No 7313 and No 8294.
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Käärik, M., Käärik, E. (2010). Imputation by Gaussian Copula Model with an Application to Incomplete Customer Satisfaction Data. In: Lechevallier, Y., Saporta, G. (eds) Proceedings of COMPSTAT'2010. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2604-3_48
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DOI: https://doi.org/10.1007/978-3-7908-2604-3_48
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