  This paper is about optimal estimation of the additive components of a
nonparametric, additive isotone regression model. It is shown that
asymptotically up to first order, each additive component can be estimated as
well as it could be by a least squares estimator if the other components were
known. The algorithm for the calculation of the estimator uses backfitting.
Convergence of the algorithm is shown. Finite sample properties are also
compared through simulation experiments.
