Estimation–Calibration of Continuous-Time Non-Homogeneous Markov Chains with Finite State Space - Manuel L. Esquível; Nadezhda P. Krasii; Gracinda R. Guerreiro (Mathematics, 2024)
We propose a method for fitting transition intensities to a sufficiently large set of trajectories of a continuous-time non-homogeneous Markov chain with a finite state space. Starting with simulated data computed with Gompertz–Makeham transition intensities, we apply the proposed method to fit piecewise linear intensities and then compare the transition probabilities corresponding to both the Gompertz–Makeham transition intensities and the fitted piecewise linear intensities; the main comparison result is that the order of magnitude of the average fitting error per unit time—chosen as a year—is always less than 1%, thus validating the methodology proposed.