Abstract
Multiple Imputation is a frequently used method for dealing with partial nonresponse. In this paper the use of finite Gaussian mixture models for multiple imputation in a Bayesian setting is discussed. Simulation studies are illustrated in order to show performances of the proposed method.
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Di Zio, M., Guarnera, U. (2008). On Multiple Imputation Through Finite Gaussian Mixture Models. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_14
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DOI: https://doi.org/10.1007/978-3-540-78246-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
Online ISBN: 978-3-540-78246-9
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