  Robust clustering from incomplete data is an important topic because, in many
practical situations, real data sets are heavy-tailed, asymmetric, and/or have
arbitrary patterns of missing observations. Flexible methods and algorithms for
model-based clustering are presented via mixture of the generalized hyperbolic
distributions and its limiting case, the mixture of multivariate skew-t
distributions. An analytically feasible EM algorithm is formulated for
parameter estimation and imputation of missing values for mixture models
employing missing at random mechanisms. The proposed methodologies are
investigated through a simulation study with varying proportions of synthetic
missing values and illustrated using a real dataset. Comparisons are made with
those obtained from the traditional mixture of generalized hyperbolic
distribution counterparts by filling in the missing data using the mean
imputation method.
