Dataset Information

Variable Selection in Function-on-Scalar Regression.

ABSTRACT: For regression models with functional responses and scalar predictors, it is common for the number of predictors to be large. Despite this, few methods for variable selection exist for function-on-scalar models, and none account for the inherent correlation of residual curves in such models. By expanding the coefficient functions using a B-spline basis, we pose the function-on-scalar model as a multivariate regression problem. Spline coefficients are grouped within coefficient function, and group-minimax concave penalty (MCP) is used for variable selection. We adapt techniques from generalized least squares to account for residual covariance by "pre-whitening" using an estimate of the covariance matrix, and establish theoretical properties for the resulting estimator. We further develop an iterative algorithm that alternately updates the spline coefficients and covariance; simulation results indicate that this iterative algorithm often performs as well as pre-whitening using the true covariance, and substantially outperforms methods that neglect the covariance structure. We apply our method to two-dimensional planar reaching motions in a study of the effects of stroke severity on motor control, and find that our method provides lower prediction errors than competing methods.

SUBMITTER: Chen Y

PROVIDER: S-EPMC4943585 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Variable Selection in Function-on-Scalar Regression.

Chen Yakuan Y Goldsmith Jeff J Ogden Todd T

Stat (International Statistical Institute) 20160302 1

For regression models with functional responses and scalar predictors, it is common for the number of predictors to be large. Despite this, few methods for variable selection exist for function-on-scalar models, and none account for the inherent correlation of residual curves in such models. By expanding the coefficient functions using a <i>B</i>-spline basis, we pose the function-on-scalar model as a multivariate regression problem. Spline coefficients are grouped within coefficient function, a ...[more]

PMID: 27429751

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Dataset Information

Variable Selection in Function-on-Scalar Regression.

Publications

Variable Selection in Function-on-Scalar Regression.

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