Distribution mixtures, a reduced-bias estimation algorithm

We focus on the definition of a new optimization criteria for mixtures of distributions estimation based on an evolution of the K-Product criterion [5]. For the case of monovariate observations we show that the new proposed criterion does not have any local non-global minimizer. This property is also observed for multivariate observations. The relevance of the new K-Product criterion is theoretically studied and analyzed through simulations (in some monovariate cases). We show that for a mixture of three separate uniform components, the distance between the criterion unique minimizer and the true component expectations is less than half the components standard deviation.