Function-on-Function Regression for Two-Dimensional Functional Data
Document Type
Article
Publication Date
10-21-2018
Journal / Book Title
Communications in Statistics-Simulation and Computation
Abstract
We present methods for modeling and estimation of a concurrent functional regression when the predictors and responses are two-dimensional functional datasets. The implementations use spline basis functions and model fitting is based on smoothing penalties and mixed model estimation. The proposed methods are implemented in available statistical software, allow the construction of confidence intervals for the bivariate model parameters, and can be applied to completely or sparsely sampled responses. Methods are tested to data in simulations and they show favorable results in practice. The usefulness of the methods is illustrated in an application to environmental data.
DOI
10.1080/03610918.2017.1353619
MSU Digital Commons Citation
Ivanescu, Andrada, "Function-on-Function Regression for Two-Dimensional Functional Data" (2018). Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works. 64.
https://digitalcommons.montclair.edu/appliedmath-stats-facpubs/64
Published Citation
Ivanescu, A. E. (2018). Function-on-function regression for two-dimensional functional data. Communications in Statistics-Simulation and Computation, 47(9), 2656-2669.