This paper examines distributional properties and predictive performance of the estimated maximum agreement linear predictor (MALP) introduced in Bottai, Kim, Lieberman, Luta, and Pena (2022) paper in The American Statistician, which is 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). Finite-sample and asymptotic properties are obtained, and confidence intervals are also presented. The predictors are illustrated using two real data sets: an eye data set and a bodyfat data set. The 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.

翻译：本文研究了Bottai、Kim、Lieberman、Luta和Pena在《The American Statistician》期刊（2022年）中提出的估计最大一致性线性预测器（MALP）的分布性质及预测性能，该预测器是最大化预测变量与响应变量之间Lin协调相关系数（CCC）的线性预测器。通过理论分析与计算机实验，将其与估计最小二乘线性预测器（LSLP）进行比较和对比。研究获得了有限样本和渐近性质，并给出了置信区间。利用两个真实数据集（眼睛数据集和体脂数据集）对预测器进行了实例说明。结果表明，若希望预测值能与响应值具有更高的一致性（以CCC衡量），则估计MALP是估计LSLP的可行替代方案。