Dataset Information

Additive Function-on-Function Regression.

ABSTRACT: We study additive function-on-function regression where the mean response at a particular time point depends on the time point itself, as well as the entire covariate trajectory. We develop a computationally efficient estimation methodology based on a novel combination of spline bases with an eigenbasis to represent the trivariate kernel function. We discuss prediction of a new response trajectory, propose an inference procedure that accounts for total variability in the predicted response curves, and construct pointwise prediction intervals. The estimation/inferential procedure accommodates realistic scenarios, such as correlated error structure as well as sparse and/or irregular designs. We investigate our methodology in finite sample size through simulations and two real data applications. Supplementary Material for this article is available online.

SUBMITTER: Kim JS

PROVIDER: S-EPMC5956967 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Additive Function-on-Function Regression.

Kim Janet S JS Staicu Ana-Maria AM Maity Arnab A Carroll Raymond J RJ Ruppert David D

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20170719 1

We study additive function-on-function regression where the mean response at a particular time point depends on the time point itself, as well as the entire covariate trajectory. We develop a computationally efficient estimation methodology based on a novel combination of spline bases with an eigenbasis to represent the trivariate kernel function. We discuss prediction of a new response trajectory, propose an inference procedure that accounts for total variability in the predicted response curve ...[more]

PMID: 29780218

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Dataset Information

Additive Function-on-Function Regression.

Publications

Additive Function-on-Function Regression.

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