Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies

DOI10.1111/biom.12314zbMath1419.62371arXiv1402.2679OpenAlexW1892222399WikidataQ40978959 ScholiaQ40978959MaRDI QIDQ2803506

Debashis Ghosh, Wen-Yu Hua

Publication date: 4 May 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.2679

zbMATH Keywords

confoundingpermutation testdistance covarianceHilbert-Schmidt independence criterionneuroimaging genomics

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (3)

Marginal Distance and Hilbert-Schmidt Covariances-Based Independence Tests for Multivariate Functional Data ⋮ Distance-based and RKHS-based dependence metrics in high dimension ⋮ A regression perspective on generalized distance covariance and the Hilbert-Schmidt independence criterion

Cites Work

• Measuring and testing dependence by correlation of distances
• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• Brownian distance covariance
• Hilbert space embeddings and metrics on probability measures
• On Distance-Based Permutation Tests for Between-Group Comparisons
• The Measurement of Observer Agreement for Categorical Data
• A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
• Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models
• Some distance properties of latent root and vector methods used in multivariate analysis

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