Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models

Abstract: We are concerned in clustering continuous data sets subject to non-ignorable missingness. We perform clustering with a specific semi-parametric mixture, under the assumption of conditional independence given the component. The mixture model isused for clustering and not for estimating the density of the full variables (observed and unobserved), thus we do not need other assumptions on the component distribution neither to specify the missingness mechanism. Estimation is performed by maximizing an extension of smoothed likelihood allowing missingness. This optimization is achieved by a Majorization-Minorization algorithm. We illustrate the relevance of our approach by numerical experiments. Under mild assumptions, we show the identifiability of the model defining the distribution of the observed data and the monotony of the algorithm. We also propose an extension of this new method to the case of mixed-type data that we illustrate on a real data set.