Change-point estimation in piecewise constant regression models with random effects

We propose an iterative algorithm to estimate change-points in general regression models. The algorithm avoids grid search to obtain maximum likelihood estimates, and thus it guarantees moderate computational time regardless of the sample size and the number of change-points to be estimated. Furthermore, it allows estimation in random effects models, where grid search is unfeasible. We present the proposed approach in practice by analyzing variations of lung functionality on a sample of transplant recipients.