An explained sum of squares approach to nonparametric regression with measurement error

Abstract

We introduce a new method in nonparametric regression problems in the presence of measurement error, known as the explained sum of squares. We discuss its theoretical properties and provide evidence of practical application in both univariate and multivariate settings. We show that our estimator is theoretically consistent, and is a novel approach to typical deconvolution methods popular in the measurement error literature.