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Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models
- Yue, Chen, Chen, Shaojie, Sair, Haris I., Airan, Raag, Caffo, Brian S.
- Computational statistics & data analysis 2015 v.89 pp. 126-133
- Markov chain, algorithms, data collection, models
- 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.