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

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

Research output: Contribution to journalArticle

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 languageEnglish (US)
Article number6045
Pages (from-to)126-133
Number of pages8
JournalComputational Statistics and Data Analysis
Volume89
DOIs
StatePublished - Sep 1 2015

Fingerprint

Intraclass Correlation Coefficient
Probit
Linear Mixed Model
Markov processes
Reproducibility
Experiments
Monte Carlo EM Algorithm
Linear Mixed Effects Model
Markov Chain Monte Carlo
Graphics
Experiment
Simulation

Keywords

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

ASJC Scopus subject areas

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Statistics and Probability
  • Applied Mathematics

Cite this

Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models. / Yue, Chen; Chen, Shaojie; Sair, Haris; Airan, Raag; Caffo, Brian S.

In: Computational Statistics and Data Analysis, Vol. 89, 6045, 01.09.2015, p. 126-133.

Research output: Contribution to journalArticle

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