Statistical analysis with missing data
Statistical analysis with missing data
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Efficient ML estimation of the multivariate normal distribution from incomplete data
Journal of Multivariate Analysis
Robust mixture modelling using the t distribution
Statistics and Computing
Bayesian analysis of mixture modelling using the multivariate t distribution
Statistics and Computing
Robust mixture modelling using multivariate t-distribution with missing information
Pattern Recognition Letters
Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm
Statistics and Computing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Empirical Study of a Linear Regression Combiner on Multi-class Data Sets
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
An efficient image pattern recognition system using an evolutionary search strategy
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Selection-fusion approach for classification of datasets with missing values
Pattern Recognition
IEEE Transactions on Information Forensics and Security
Mixtures of common factor analyzers for high-dimensional data with missing information
Journal of Multivariate Analysis
Infinite Dirichlet mixture models learning via expectation propagation
Advances in Data Analysis and Classification
Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition
Computational Statistics & Data Analysis
Automated learning of factor analysis with complete and incomplete data
Computational Statistics & Data Analysis
| Hi-index | 0.01 |
It is an important research issue to deal with mixture models when missing values occur in the data. In this paper, computational strategies using auxiliary indicator matrices are introduced for efficiently handling mixtures of multivariate normal distributions when the data are missing at random and have an arbitrary missing data pattern, meaning that missing data can occur anywhere. We develop a novel EM algorithm that can dramatically save computation time and be exploited in many applications, such as density estimation, supervised clustering and prediction of missing values. In the aspect of multiple imputations for missing data, we also offer a data augmentation scheme using the Gibbs sampler. Our proposed methodologies are illustrated through some real data sets with varying proportions of missing values.