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

furio urso, a.a. (2021). Model selection procedure for mixture hidden Markov models. In CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS (pp. 243-246) [10.36253/978-88-5518-340-6].

Model selection procedure for mixture hidden Markov models

furio urso
;
antonino abbruzzo;maria francesca cracolici
2021-01-01

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
2021
Settore SECS-S/01 - Statistica
9788855183406
furio urso, a.a. (2021). Model selection procedure for mixture hidden Markov models. In CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS (pp. 243-246) [10.36253/978-88-5518-340-6].
File in questo prodotto:
File Dimensione Formato  
19407 (1).pdf

accesso aperto

Tipologia: Versione Editoriale
Dimensione 754.34 kB
Formato Adobe PDF
754.34 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/582796
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact