Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure

Biometrics
Yanming LiJi Zhu

Abstract

We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study.

References

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Citations

Sep 12, 2015·Bioinformatics·Benoît LiquetRodolphe Thiébaut
Jul 25, 2017·Statistical Applications in Genetics and Molecular Biology·Haixiang ZhangLei Liu
Dec 22, 2017·Bioinformatics·Joshua MayerRanadip Pal
Dec 7, 2018·Prevention Science : the Official Journal of the Society for Prevention Research·Tyson S Barrett, Ginger Lockhart
May 26, 2020·Applied Microbiology and Biotechnology·Leena MalayilAmy R Sapkota
May 25, 2021·Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·Yuan FengEric C Chi

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