Model selection procedure for mixture hidden Markov models
- Autori: furio urso, antonino abbruzzo, maria francesca cracolici
- Anno di pubblicazione: 2021
- Tipologia: Contributo in atti di convegno pubblicato in volume
- OA Link: http://hdl.handle.net/10447/582796
Abstract
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