Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models
Methodology
2013-11-20 v2
Abstract
Data reproducibility is a critical issue in all scientific experiments. In this manuscript, we consider the problem of quantifying the reproducibility of graphical measurements. We generalize the concept of image intra-class correlation coefficient (I2C2) and propose the concept of the graphical intra-class correlation coefficient (GICC) for such purpose. The concept of GICC is based on multivariate probit-linear mixed effect models. We will present a Markov Chain EM (MCEM) algorithm for estimating the GICC. Simulations results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.
Keywords
Cite
@article{arxiv.1311.4210,
title = {Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models},
author = {Chen Yue and Shaojie Chen and Haris I. Sair and Raag Airan and Brian S. Caffo},
journal= {arXiv preprint arXiv:1311.4210},
year = {2013}
}
Comments
14 pages, 3 figures, 1 table