Statistical analysis with missing data
Statistical analysis with missing data
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Robust mixture modelling using multivariate t-distribution with missing information
Pattern Recognition Letters
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Imputation through finite Gaussian mixture models
Computational Statistics & Data Analysis
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
Nearest neighbours in least-squares data imputation algorithms with different missing patterns
Computational Statistics & Data Analysis
Learning mixture models via component-wise parameter smoothing
Computational Statistics & Data Analysis
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Expert Systems with Applications: An International Journal
Kml: A package to cluster longitudinal data
Computer Methods and Programs in Biomedicine
Retail clients latent segments
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
A probabilistic approach to finding geometric objects in spatial datasets of the milky way
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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One difficulty with classification studies is unobserved or missing observations that often occur in multivariate datasets. The mixture likelihood approach to clustering has been well developed and is much used, particularly for mixtures where the component distributions are multivariate normal. It is shown that this approach can be extended to analyse data with mixed categorical and continuous attributes and where some of the data are missing at random in the sense of Little and Rubin (Statistical Analysis with Mixing Data, Wiley, New York).