Parsimonious parameterization of correlation matrices using truncated vines and factor analysis

DOI10.1016/j.csda.2014.03.002zbMath1506.62028OpenAlexW2085474454MaRDI QIDQ1623595

Eike Christian Brechmann, Joe, Harry

Publication date: 23 November 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2014.03.002

zbMATH Keywords

EM algorithmmultivariate normalpartial correlationsMarkov treesregular vines

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to actuarial sciences and financial mathematics (62P05) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items

Approximate likelihood with proxy variables for parameter estimation in high-dimensional factor copula models ⋮ Prediction based on conditional distributions of vine copulas ⋮ Factor tree copula models for item response data ⋮ A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series ⋮ Common sampling orders of regular vines with application to model selection ⋮ Representing Sparse Gaussian DAGs as Sparse R-Vines Allowing for Non-Gaussian Dependence ⋮ Selection of sparse vine copulas in high dimensions with the Lasso ⋮ Truncation of vine copulas using fit indices

Uses Software

• psych

Cites Work

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