Sparse semiparametric canonical correlation analysis for data of mixed types

DOI10.1093/BIOMET/ASAA007zbMath1451.62051arXiv1807.05274OpenAlexW3016700505MaRDI QIDQ5127209

Grace Yoon, Irina Gaynanova, Raymond J. Carroll

Publication date: 21 October 2020

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.05274

zbMATH Keywords

Bayesian information criterionzero-inflated dataGaussian copula modelKendall's \(\tau\)latent correlation matrixtruncated continuous variable

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Protein sequences, DNA sequences (92D20)

Related Items (4)

Decomposition of Variation of Mixed Variables by a Latent Mixed Gaussian Copula Model ⋮ Sparse regression for large data sets with outliers ⋮ Fast Computation of Latent Correlations ⋮ Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks

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