Varying-coefficient functional linear regression

Yichao Wu Jianqing Fan Hans-Georg Mller

Statistics Theory and Methods mathscidoc:1912.43336

Bernoulli, 16, (3), 730-758, 2010
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our
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  title={Varying-coefficient functional linear regression},
  author={Yichao Wu, Jianqing Fan, and Hans-Georg Mller},
Yichao Wu, Jianqing Fan, and Hans-Georg Mller. Varying-coefficient functional linear regression. 2010. Vol. 16. In Bernoulli. pp.730-758.
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