Abstract
Joinpoint regression models are popular in various situations (modeling different trends in economics, mortality and incidence series or epidemiology studies and clinical trials). The literature on joinpoint regression mostly focuses on either the frequentist point of view, or discusses Bayesian approaches instead.
A model selection step in all these scenarios considers only some limited set of alternatives, from which the final model is chosen. We present a different model estimation approach: the final model is selected out of all possible alternatives admitted by the data.
We apply the L1L1-regularization idea and via the sparsity principle we identify significant joinpoint locations to construct the final model. Some theoretical results and practical examples are given as well.