Hidden Markov Model Based on Logistic Regression

Byeongheon Lee, Joowon Park and Yongku Kim ()
Additional contact information
Byeongheon Lee: Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea
Joowon Park: School of Forest Science and Landscape Architecture, Kyungpook National University, Daegu 41566, Republic of Korea
Yongku Kim: Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea

Mathematics, 2023, vol. 11, issue 20, 1-12

Abstract: A hidden Markov model (HMM) is a useful tool for modeling dependent heterogeneous phenomena. It can be used to find factors that affect real-world events, even when those factors cannot be directly observed. HMMs differ from traditional methods by using state variables and mixture distributions to model the hidden states. This allows HMMs to find relationships between variables even when the variables cannot be directly observed. HMM can be extended, allowing the transition probabilities to depend on covariates. This makes HMMs more flexible and powerful, as they can be used to model a wider range of sequential data. Modeling covariates in a hidden Markov model is particularly difficult when the dimension of the state variable is large. To avoid these difficulties, Markovian properties are achieved by implanting the previous state variables to the logistic regression model. We apply the proposed method to find the factors that affect the hidden state of matsutake mushroom growth, in which it is hard to find covariates that directly affect matsutake mushroom growth in Korea. We believe that this method can be used to identify factors that are difficult to find using traditional methods.

Keywords: Bayesian analysis; hidden Markov model; hierarcical modeling; logistic regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/20/4396/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/20/4396/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:20:p:4396-:d:1265312

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().