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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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    1. Guangyong Zou & Allan Donner, 2004. "Confidence Interval Estimation of the Intraclass Correlation Coefficient for Binary Outcome Data," Biometrics, The International Biometric Society, vol. 60(3), pages 807-811, September.
    2. Ravi Varadhan & Christophe Roland, 2008. "Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 335-353, June.
    3. Martin S. Ridout & Clarice G. B. Demétrio & David Firth, 1999. "Estimating Intraclass Correlation for Binary Data," Biometrics, The International Biometric Society, vol. 55(1), pages 137-148, March.
    4. Horrace, William C., 2005. "Some results on the multivariate truncated normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 94(1), pages 209-221, May.
    5. German Rodriguez & Irma Elo, 2003. "Intra-class correlation in random-effects models for binary data," Stata Journal, StataCorp LP, vol. 3(1), pages 32-46, March.
    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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