Abstract:
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In many social and clinical studies, Lin’s (1989) concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. Most commonly, it is used under the assumption that data are normally distributed. However, in many practical applications, data are often skewed and/or thick-tailed. Tashakor and Chinchilli(2018) proposed robust estimation methods of alternative CCC indices by focusing on functionals that yield robust L-statistics. They used Trimmed mean and Winsorized mean as fuctionals of L-statistics. In this work, we investigate the effect of different degrees of trimming on the bias and coverage probability of the proposed robust coefficient. We provide two data examples to illustrate the methodology, and we discuss the results of computer simulation studies that evaluate statistical performance.
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