Order selection in multidimensional finite mixture models
Tudor Manole · Jan 20, 2017
Date: 2017-01-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: Finite mixture models provide a natural framework for analyzing data from heterogeneous populations. In practice, however, the number of hidden subpopulations in the data may be unknown. The problem of estimating the order of a mixture model, namely the number of subpopulations, is thus crucial for many applications. In this talk, we present a new penalized likelihood solution to this problem, which is applicable to models with a multidimensional parameter space.