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

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

doi:10.1016/j.csda.2015.02.012

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

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

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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.

Original language	English (US)
Article number	6045
Pages (from-to)	126-133
Number of pages	8
Journal	Computational Statistics and Data Analysis
Volume	89
DOIs	https://doi.org/10.1016/j.csda.2015.02.012
State	Published - Sep 1 2015

Keywords

Graphical intra class correlation coefficient
MCMCEM
Multivariate probit-linear mixed model

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/j.csda.2015.02.012

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abstract = "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.",

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AU - Yue, Chen

AU - Chen, Shaojie

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AU - Airan, Raag

AU - Caffo, Brian S.

PY - 2015/9/1

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N2 - 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.

AB - 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.

KW - Graphical intra class correlation coefficient

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

Abstract

Keywords

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