Maximum Agreement Linear Predictors

TL;DR

This paper introduces the maximum agreement linear predictor (MALP), which maximizes concordance with the true values, and compares it to least-squares predictors through theoretical analysis and real data applications.

Contribution

It provides a detailed analysis of MALP's properties, compares it with LSLP, and demonstrates its effectiveness in achieving higher agreement with observed data.

Findings

MALP has favorable distributional properties and predictive performance.

MALP often yields higher agreement with true values than LSLP.

The paper includes real data examples illustrating MALP's advantages.

Abstract

This paper studies predictor functions motivated by maximizing a measure of agreement with the predictand. Specifically, it examines distributional properties and predictive performance of the estimated maximum agreement linear predictor (MALP), the linear predictor maximizing Lin's concordance correlation coefficient (CCC) between the predictor and the predictand. It is compared and contrasted, theoretically and through computer experiments, with the estimated least-squares linear predictor (LSLP), with respect to some performance measures. Finite-sample and asymptotic properties are obtained, and confidence intervals and prediction intervals are also presented. Predictors are illustrated using two real data sets: an eye data set and a body fat data set. Results indicate that the estimated MALP is a viable alternative to the estimated LSLP if one desires a predictor whose predicted values possesses higher agreement with the predictand values, as measured by the CCC.