Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models

Chen Yue, Shaojie Chen, Haris I. Sair, Raag Airan and Brian S. Caffo

Computational Statistics & Data Analysis, 2015, vol. 89, issue C, 126-133

Abstract: Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcmcEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test–retest dataset.

Keywords: Graphical intra class correlation coefficient; Multivariate probit-linear mixed model; MCMCEM (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:89:y:2015:i:c:p:126-133

DOI: 10.1016/j.csda.2015.02.012

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