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

DOI10.1016/j.csda.2015.02.012zbMath1468.62225arXiv1311.4210OpenAlexW2119315205WikidataQ40719284 ScholiaQ40719284MaRDI QIDQ1663261

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

Publication date: 21 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1311.4210

zbMATH Keywords

graphical intra class correlation coefficientMCMCEMmultivariate probit-linear mixed model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Probabilistic graphical models (62H22)

Uses Software

• Stata
• tmvtnorm

Cites Work

• Unnamed Item
• Some results on the multivariate truncated normal distribution
• Multilevel functional principal component analysis
• Graph similarity scoring and matching
• Confidence Interval Estimation of the Intraclass Correlation Coefficient for Binary Outcome Data
• Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm
• Maximum Likelihood Variance Components Estimation for Binary Data
• Maximum Likelihood Estimation for Probit-Linear Mixed Models with Correlated Random Effects
• A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching
• Estimating Intraclass Correlation for Binary Data
• Ascent-Based Monte Carlo Expectation– Maximization

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