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A0795
Title: Predictive model for sparse longitudinal data Authors:  Shixuan Wang - Miami Univeristy (United States)
Seonjin Kim - Miami University (United States) [presenting]
Hyunkeun Cho - University of Iowa (United States)
Won Chang - University of Cincinnati (United States)
Abstract: A multivariate function-on-function kernel-based estimator is proposed to predict the mean response trajectory for sparse and irregularly measured longitudinal data. The kernel function is constructed by weighing in the subject-wise similarity on $L_2$ metric space between predictor trajectories, where we assume an analogous fashion in predictor trajectories over time would result in a similar trend in the response trajectory among subjects. In order to deal with the curse of dimensionality caused by the multiple predictors, we propose a novel multiplicative model with multivariate Gaussian kernels. This model is capable of achieving dimension reduction as well as selecting functional covariates with predictive significance. The asymptotic properties of the proposed nonparametric estimator are investigated under mild regularity conditions. We illustrate the robustness and the flexibility of our proposed methods via the simulation study and an application to the Framingham Heart Study.