Hidden Markov Linear Regression Model and its Parameter Estimation

This article first defines a hidden Markov linear regression model for the purpose of further studying the mutual transformation between different states in the linear regression model, and the regression relationship between the dependent variable and the independent variable in each state. And the...

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Main Authors: Hefei Liu, Kunqjnu Wang, Yong Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9222161/
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author Hefei Liu
Kunqjnu Wang
Yong Li
author_facet Hefei Liu
Kunqjnu Wang
Yong Li
author_sort Hefei Liu
collection DOAJ
description This article first defines a hidden Markov linear regression model for the purpose of further studying the mutual transformation between different states in the linear regression model, and the regression relationship between the dependent variable and the independent variable in each state. And then, K-means clustering analysis methods are used to identify the hidden states of observed data, and the maximum likelihood estimation of the hidden state transition probability matrix elements is obtained by using the maximum likelihood estimation method, and parameter estimation of unknown parameters in linear regression model is also presented by using the least squares method. Finally, the observation vector set is generated according to the defined model, and the empirical simulation demonstrates that the parameter estimation method shown in this work is reliable.
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spelling doaj.art-56d7dd0f641f46b7b4c57a5b9019ab642022-12-21T20:18:54ZengIEEEIEEE Access2169-35362020-01-01818703718704210.1109/ACCESS.2020.30307769222161Hidden Markov Linear Regression Model and its Parameter EstimationHefei Liu0Kunqjnu Wang1Yong Li2https://orcid.org/0000-0003-1325-001XSchool of Mathematics and Statistics, Qujing Normal University, Qujing, ChinaSchool of Mathematics and Statistics, Qujing Normal University, Qujing, ChinaSchool of Information and Engineering, Qujing Normal University, Qujing, ChinaThis article first defines a hidden Markov linear regression model for the purpose of further studying the mutual transformation between different states in the linear regression model, and the regression relationship between the dependent variable and the independent variable in each state. And then, K-means clustering analysis methods are used to identify the hidden states of observed data, and the maximum likelihood estimation of the hidden state transition probability matrix elements is obtained by using the maximum likelihood estimation method, and parameter estimation of unknown parameters in linear regression model is also presented by using the least squares method. Finally, the observation vector set is generated according to the defined model, and the empirical simulation demonstrates that the parameter estimation method shown in this work is reliable.https://ieeexplore.ieee.org/document/9222161/Hidden markov modellinear regression modelcluster analysisparameter estimation
spellingShingle Hefei Liu
Kunqjnu Wang
Yong Li
Hidden Markov Linear Regression Model and its Parameter Estimation
IEEE Access
Hidden markov model
linear regression model
cluster analysis
parameter estimation
title Hidden Markov Linear Regression Model and its Parameter Estimation
title_full Hidden Markov Linear Regression Model and its Parameter Estimation
title_fullStr Hidden Markov Linear Regression Model and its Parameter Estimation
title_full_unstemmed Hidden Markov Linear Regression Model and its Parameter Estimation
title_short Hidden Markov Linear Regression Model and its Parameter Estimation
title_sort hidden markov linear regression model and its parameter estimation
topic Hidden markov model
linear regression model
cluster analysis
parameter estimation
url https://ieeexplore.ieee.org/document/9222161/
work_keys_str_mv AT hefeiliu hiddenmarkovlinearregressionmodelanditsparameterestimation
AT kunqjnuwang hiddenmarkovlinearregressionmodelanditsparameterestimation
AT yongli hiddenmarkovlinearregressionmodelanditsparameterestimation