Penalized estimation in high-dimensional hidden Markov models with state-specific graphical models

We consider penalized estimation in hidden Markov models (HMMs) with multivariate Normal observations. In the moderate-to-large dimensional setting, estimation for HMMs remains challenging in practice, due to several concerns arising from the hidden nature of the states. We address these concerns by $\ell_1$-penalization of state-specific inverse covariance matrices. Penalized estimation leads to sparse inverse covariance matrices which can be interpreted as state-specific conditional independence graphs. Penalization is nontrivial in this latent variable setting; we propose a penalty that automatically adapts to the number of states $K$ and the state-specific sample sizes and can cope with scaling issues arising from the unknown states. The methodology is adaptive and very general, applying in particular to both low- and high-dimensional settings without requiring hand tuning. Furthermore, our approach facilitates exploration of the number of states $K$ by coupling estimation for successive candidate values $K$. Empirical results on simulated examples demonstrate the effectiveness of the proposed approach. In a challenging real data example from genome biology, we demonstrate the ability of our approach to yield gains in predictive power and to deliver richer estimates than existing methods.