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Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process.

ABSTRACT: This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially-varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian proess prior leads to the posterior consistency for parameter estimation and variable selection for scalar-on-image regression, even when the number of predictors is larger than the sample size. The proposed method is compared to alternatives via simulation and applied to an electroen-cephalography study of alcoholism.

SUBMITTER: Kang J

PROVIDER: S-EPMC6345249 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process.

Kang Jian J Reich Brian J BJ Staicu Ana-Maria AM

Biometrika 20180119 1

This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially-varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian proess prior leads to the posterior co ...[more]

PMID: 30686828

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