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

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

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.

Suggested Citation

  • Yue, Chen & Chen, Shaojie & Sair, Haris I. & Airan, Raag & Caffo, Brian S., 2015. "Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 126-133.
    • Handle: RePEc:eee:csdana:v:89:y:2015:i:c:p:126-133
    DOI: 10.1016/j.csda.2015.02.012
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    References listed on IDEAS

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    6. Brian S. Caffo & Wolfgang Jank & Galin L. Jones, 2005. "Ascent‐based Monte Carlo expectation– maximization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 235-251, April.
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