Agreement for curved data.

An agreement problem usually involves assessing the concordance of two sets of measurements, and the problem covers a broad range of data. In practice, the observations are often curves instead of the traditional points. In this article, the agreement problem is studied for curved data. Following the rationale in constructing a correlation coefficient curve for heterocorrelaticity, an agreement curve is proposed to measure agreement as a function of the independent variable for curved data. The agreement curve overcomes the drawback when only one index is used in assessing the agreement of two measurements, and it covers all situations including the nonconstant mean, nonhomogenous variance, and the data range. A real dataset is used to demonstrate the approach and to show accurate assessment and information gained if curved data are used.