parsimonious mixture of mean-mixture of normal distributions with missing data

Clustering multivariate data based on mixture distributions is a usual method to characterize groups and label data sets. mixture models have recently been received considerable attention to accommodate asymmetric and missing data via exploiting skewed and heavy-tailed distributions. in this paper, a mixture of multivariate mean-mixture of normal distributions is considered for handling missing data. the em-type algorithms are carried out to determine maximum likelihood of parameters estimations. we analyzed the real data sets and conducted simulation studies to demonstrate the superiority of the proposed methodology.