2026/01/01 by Taeho Kim, Pierre Chaussé, Matteo Bottai +4 · 1 voice
Decision Sciences · Medicine · Mathematics · #Reliability and Agreement in Measurement #Hemodynamic Monitoring and Therapy #Statistical Methods in Epidemiology
paper · doi:10.1214/26-ejs2550
openalex publication_date 2026/01/01 · openalex created_date 2026/07/08 · openalex updated_date 2026/07/08
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. The impact of the plug-in approach, where unknown parameters are replaced by estimators in forming the predictors, is also examined. 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 possess higher agreement with the predictand values, as measured by the CCC. The proposed predictor could be used, for instance, in missing value settings where imputation is needed; or in calibration problems where the goal is to have good agreement between measures obtained via a gold standard method, but which could be expensive and/or hard-to-implement, and those obtained through alternative cheaper and/or easier-to-implement methods.