B0513
Title: Model-based clustering of multinomial count data under the presence of covariates
Authors: Panagiotis Papastamoulis - Athens University of Economics and Business (Greece) [presenting]
Abstract: The problem of inferring an unknown number of clusters in multinomial count data is considered by estimating finite mixtures of multinomial distributions with or without covariates. Both Maximum Likelihood (ML), as well as Bayesian estimation, are taken into account. Under a Maximum Likelihood approach, we provide an Expectation-Maximization (EM) algorithm which exploits a careful initialization procedure combined with a ridge-stabilized implementation of the Newton-Raphson method in the M-step. Under a Bayesian setup, a stochastic gradient Markov chain Monte Carlo (MCMC) algorithm embedded within a prior parallel tempering scheme is devised. The number of clusters is selected according to the Integrated Completed Likelihood criterion in the ML approach and estimating the number of non-empty components in overfitting mixture models in the Bayesian case.
