Maximum Agreement Linear Prediction via the Concordance Correlation Coefficient

Music City Center 

We examine distributional properties and predictive performance of the estimated maximum agreement linear predictor (MALP), 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), with respect to some performance measures. Finite-sample and asymptotic properties are obtained, and confidence intervals and prediction intervals are also presented. The predictors are illustrated using two real data sets: an eye data set and a body fat 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.

prediction

agreement

Lin's concordance correlation coefficient