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Activity Number: 614 - Statistical Methods for Longitudinal and Other Dependent Data
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #302897
Title: Robust Matrix-Based Measures of Agreement Based on L-Statistics for Repeated Measures
Author(s): Elahe Tashakor* and Vernon Chinchilli
Companies: Pennsylvania State University and Pennsylvania State University
Keywords: L-statistics; agreement; robust estimation; multivariate

The concordance correlation coefficient is commonly used to assess agreement between two raters or two methods of measuring a response when the data are measured on a continuous scale. It typically is used under the assumption that data are normally distributed. However, in many practical applications, data often are skewed and/or thick-tailed. Previously, we have proposed an approach that extends existing methods of robust estimators to produce more robust versions of the concordance correlation coefficient. In this paper we extend the application of this class of estimators to a multivariate situation, possibly repeated measurements, based on a matrix norm that possesses the properties needed to characterize the level of agreement between two p × 1 vectors of random variables. We provide two data examples to illustrate the methodology, and we discuss the results of computer simulation studies that evaluate statistical performance.

Authors who are presenting talks have a * after their name.

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