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

被引：3

作者：

Yue, Chen ^{[1
]}

Chen, Shaojie ^{[1
]}

Sair, Hans I. ^{[2
]}

Airan, Raag ^{[2
]}

Caffo, Brian S. ^{[1
]}

机构：

[1] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21205 USA

[2] Johns Hopkins Univ, Dept Radiol & Radiol Sci, Baltimore, MD 21205 USA

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2015年 / 89卷

关键词：

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

D O I：

10.1016/j.csda.2015.02.012

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：126 / 133

页数：8

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