Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 51, Issue 11, Pages 5305-5316Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2006.10.002
Keywords
incomplete data; imputation; nearest neighbour donor
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Imputation is a widely used method for handling missing data. It consists in the replacement of missing values with plausible ones. Parametric and nonparametric techniques are generally adopted for modelling incomplete data. Both of them have advantages and drawbacks. Parametric techniques are parsimonious but depend on the model assumed, while nonparametric techniques are more flexible but require a high amount of observations. The use of finite mixture of multivariate Gaussian distributions for handling missing data is proposed. The main reason is that it allows to control the trade-off between parsimony and flexibility. An experimental comparison with the widely used imputation nearest neighbour donor is illustrated. (C) 2006 Elsevier B.V. All rights reserved.
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