Clustering Data with nonignorable Missingness using Semi-Parametric Mixture Models

Marie Du Roy de Chaumaray, Matthieu Marbac

Abstract:

We are concerned in clustering continuous data
sets subject to nonignorable missingness. We per-
form clustering with a specific semi-parametric
mixture, avoiding the component distributions
and the missingness process to be specified, un-
der the assumption of conditional independence
given the component. Estimation is performed
by maximizing an extension of smoothed likeli-
hood allowing missingness. This optimization
is achieved by a Majorization-Minorization algo-
rithm. We illustrate the relevance of the approach
by numerical experiments.

Keywords: clustering, mixture models, nonignorable missingness, nonparametric mixture