Order Selection with Confidence for Mixture Models
Date: Friday 19 March 2021
Time: 3pm
Speaker: Dr Hien Nguyen (La Trobe University)
Abstract:
Finite mixture models are distribution models that are defined by convex combinations of a finite number of elements (components) from some base distribution class, where the number of elements dictates the complexity of the mixture model. Given that data arise from a class of finite mixture models, where the number of components is unknown, an important problem that arises is choice of the number of components that one should use to model the data. We present a hypothesis test-based algorithm to selecting the number of components of a mixture model that yields a lower bound on the number of components, with confidence. We demonstrate that in special circumstances, the approach also yields a method that consistently selects the correct number of components, and we demonstrate the effectiveness of the approach via a study of the class problem of order selection for finite mixtures of Gaussian distributions.
Zoom Link: