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:本文考察了Bottai, Kim, Lieberman, Luta, 和 Pena (2022) 在《美国统计学家》杂志中提出的最大一致性线性预测(MALP)的分布特性和预测性能,它是一种线性预测器,能够最大化预测器和待预测变量之间的Lin一致性相关系数(CCC)。本文从理论和计算实验的角度进行比较和对比,与最小二乘线性预测器(LSLP)进行了对比。得到了有限样本和渐近特性,并提供了置信区间。结果表明,估计的MALP是LSLP的可行替代品,如果希望预测值与预测变量值具有更高的CCC一致性,那么它可以成为一个好的选择。最后,本文采用了眼部数据集和体脂数据集进行了预测器的演示。