Additive Function-on-Function Regression
Autor: | Kim, Janet S., Staicu, Ana-Maria, Maity, Arnab, Carroll, Raymond J., Ruppert, David |
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Rok vydání: | 2016 |
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Druh dokumentu: | Working Paper |
Popis: | 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. Comment: 26 pages, 4 figures |
Databáze: | arXiv |
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