.png){kind=icon}
.png){kind=icon}
Return
Hidden Markov Quantile Graphical Models
.png){kind=icon}
PRE
AI
DOI：10.1080/10618600.2025.2579526.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article introduces a novel hidden Markov quantile graphical model for capturing time-varying conditional dependence structures in multivariate time series. The proposed method allows the identification of state-specific graphs and the dynamic relationships between variables across hidden regimes via joint mixtures of hidden Markov quantile regressions. We leverage the sparsity pattern of the quantile regression coefficients to recover conditional independence networks within each latent state. Estimation of model parameters is achieved through pseudo maximum likelihood using a penalized Expectation-Maximization algorithm to induce sparsity in the quantile regression coefficients. The performance of the method is validated through simulations and compared with existing approaches. The proposed model is applied to air pollution data in Northern Italy, analyzing the interdependence of PM 2.5 concentration levels across 14 major cities from 2019 to 2022.
Keywords:
Air pollution
Conditional independence
EM algorithm
Non-Gaussian data
Time-varying graphical model
Weighted quantile regression
Journal
J
IF:
1.8
Papers:
138
Citations:
6.4K