Parameter Estimation for Time-Scaled Inhomogeneous Phase-Type Distributions from Discrete Observations

Abstract

Inhomogeneous phase-type (IPH) distributions extend classical phase-type (PH) models by allowing transition intensities to vary over time, offering greater flexibility for modeling heavy-tailed distributions or time-dependent absorption phenomena. Statistical inference for these models has largely assumed that absorption times, or entire trajectories, are observed exactly. In many applications, however, the process is observed only at discrete, irregularly spaced time points, so that transition and absorption times are unknown and estimation becomes a missing-data problem. We address this setting for the subclass with time-scaled sub-intensity matrices $\mathbf{\Lambda }(t)=h_{\beta }(t)\mathbf{\Lambda }$, which admits a time transformation to a homogeneous Markov jump process (MJP). We develop an inference framework that combines Markov-bridge data augmentation with a Stochastic Expectation-Maximization (SEM) algorithm: at each iteration the latent continuous-time trajectories are simulated conditionally on the discrete observations, and the parameters are then updated by maximizing the resulting complete-data likelihood. The baseline sub-intensity matrix $\mathbf{\Lambda }$ is updated by its closed-form complete-data maximum-likelihood estimator, while the time-scaling parameter $\beta$ is refined by gradient ascent on the same complete-data log-likelihood. The reported estimators are thus obtained from complete-data maximum-likelihood updates, avoiding constrained nonlinear optimization of the observed-data likelihood. Through simulation studies for the matrix-Gompertz and matrix-Weibull families, and a real-data application to coronary allograft vasculopathy (CAV) progression, we demonstrate that the proposed approach provides an accurate and computationally tractable tool for fitting time-scaled IPH models to irregular multi-state data.