Abstract by Cason Wight
Inference for semi-Markov models with panel data
Semi-Markov processes effectively model waiting times and probabilities for many multi-state scenarios. In practice, data are collected at specific points in time where the stage of a process is observed at regular intervals. Intermittently observed measurements such as these are known as panel data. One such example is the condition of asthma patients (optimal, suboptimal, or unacceptable). Our purpose is to estimate the parameters of a semi-Markov model with panel data on asthma patients. The state-of-the-art technique uses the stochastic expectation-maximization (SEM) algorithm for inference. One setback to this method is the large computational burden of the sampling process. Our goal is to improve upon this method by either adjusting the expectation step to avoid such sampling or provide a non-parametric approach with no such limitations.