Year: 2023
Type: article
Abstract: We consider the problem of inferring an unknown number of clusters in multinomial count data, 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. Our method is illustrated in simulated data and applied to two real datasets. The proposed methods are implemented in a contributed package, available online.
Author: Panagiotis Papastamoulis
Institution: Athens University of Economics and Business
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,990
Award: G6996893898
OpenAlex ID: W4383604258