Additive Function-on-Function Regression

Autor:	Kim, Janet S., Staicu, Ana-Maria, Maity, Arnab, Carroll, Raymond J., Ruppert, David
Rok vydání:	2016
Předmět:	Statistics - Methodology
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
Externí odkaz:	http://arxiv.org/abs/1606.03775 Zobrazit plný text záznamu View this record from Arxiv