Accelerating the pool-adjacent-violators algorithm for isotonic distributional regression

In this note we describe in detail how to apply the pool-adjacent-violators algorithm (PAVA) efficiently in the context of estimating stochastically ordered distribution functions. The main idea is that the solution of a weighted monotone least squares problem changes only little if one component of the target vector to be approximated is changed.