Hidden Markov Quantile Graphical Models
提出一种隐马尔可夫分位数图模型，用于捕捉多元时间序列中随时间变化的条件依赖结构，通过分位数回归系数稀疏模式识别各隐状态下的条件独立网络，并应用于意大利北部14个城市PM2.5浓度数据的相互依赖分析。
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.