Application of the limit of truncated isotonic regression in optimization subject to isotonic and bounding constraints

Abstract

An isotonic regression truncated by confining its domain to a union of its level sets is the isotonic regression in the reduced function space. When some of the weights with which the inner product system is defined go to infinity, the truncated isotonic regression converges. This limit can be used in discribing the projection onto the set of vectors which satisfy an order restriction and have one or more of its coordinates bounded above and/or below. Through this characterization, two inequalities associated with the projection are established and found useful in order and bound restricted statistical inference. The results obtained show that for an exponential distribution family the inequalities lead to the linkage of the order and bound restricted MLE with the projection of the unrestricted MLE, and the dominance of the order and bound restricted MLE over the unrestricted MLE with respect to two classes of loss functions and risks as well as Bayes risks.