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Sparse Single Index Models for Multivariate Responses.

ABSTRACT: Joint models are popular for analyzing data with multivariate responses. We propose a sparse multivariate single index model, where responses and predictors are linked by unspecified smooth functions and multiple matrix level penalties are employed to select predictors and induce low-rank structures across responses. An alternating direction method of multipliers (ADMM) based algorithm is proposed for model estimation. We demonstrate the effectiveness of proposed model in simulation studies and an application to a genetic association study.

SUBMITTER: Feng Y

PROVIDER: S-EPMC8133682 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Sparse Single Index Models for Multivariate Responses.

Feng Yuan Y Xiao Luo L Chi Eric C EC

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20200728 1

Joint models are popular for analyzing data with multivariate responses. We propose a sparse multivariate single index model, where responses and predictors are linked by unspecified smooth functions and multiple matrix level penalties are employed to select predictors and induce low-rank structures across responses. An alternating direction method of multipliers (ADMM) based algorithm is proposed for model estimation. We demonstrate the effectiveness of proposed model in simulation studies and ...[more]

PMID: 34025100

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