  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.
