6533b7d0fe1ef96bd125b311

RESEARCH PRODUCT

Model selection procedure for mixture hidden Markov models

Furio UrsoAntonino AbbruzzoMaria Francesca Cracolici

subject

model selectionclustersinformation criteriaSettore SECS-S/01 - Statisticahidden statesentropy-based scores

description

This paper proposes a model selection procedure to identify the number of clusters and hidden states in discrete Mixture Hidden Markov models (MHMMs). The model selection is based on a step-wise approach that uses, as score, information criteria and an entropy criterion. By means of a simulation study, we show that our procedure performs better than classical model selection methods in identifying the correct number of clusters and hidden states or an approximation of them

yearjournalcountryeditionlanguage
2021-01-01
10.36253/978-88-5518-340-6https://hdl.handle.net/10447/582796