Iterative Isotonic Regression

TL;DR

This paper proposes a new iterative nonparametric regression method called Iterative Isotonic Regression (I.I.R.) that decomposes a bounded variation function into monotone components and proves its consistency.

Contribution

It introduces I.I.R., combining backfitting with isotonic regression, and establishes its theoretical consistency for estimating functions of bounded variation.

Findings

Proves the consistency of I.I.R. with increasing sample size.

Generalizes isotonic regression consistency to non-monotone functions.

Links backfitting algorithm to Von Neumann's convex analysis algorithm.

Abstract

This article introduces a new nonparametric method for estimating a univariate regression function of bounded variation. The method exploits the Jordan decomposition which states that a function of bounded variation can be decomposed as the sum of a non-decreasing function and a non-increasing function. This suggests combining the backfitting algorithm for estimating additive functions with isotonic regression for estimating monotone functions. The resulting iterative algorithm is called Iterative Isotonic Regression (I.I.R.). The main technical result in this paper is the consistency of the proposed estimator when the number of iterations $k_n$ grows appropriately with the sample size $n$. The proof requires two auxiliary results that are of interest in and by themselves: firstly, we generalize the well-known consistency property of isotonic regression to the framework of a non-monotone regression function, and secondly, we relate the backfitting algorithm to Von Neumann's algorithm in convex analysis.