Abstract
We develop fast fitting methods for generalized functional linear models. An undersmooth of the functional predictor is obtained by projecting on a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression. Our method can be applied to many functional data designs including functions measured with and without error, sparsely or densely sampled. The methods also extend to the case of multiple functional predictors or functional predictors with a natural multilevel structure. Our approach can be implemented using standard mixed effects software and is computationally fast. Our methodology is motivated by a diffusion tensor imaging (DTI) study. The aim of this study is to analyze differences between various cerebral white matter tract property measurements of multiple sclerosis (MS) patients and controls. While the statistical developments proposed here were motivated by the DTI study, the methodology is designed and presented in generality and is applicable to many other areas of scientific research. An online appendix provides R implementations of all simulations.
Disciplines
Statistical Methodology | Statistical Theory
Suggested Citation
Goldsmith, Jeff; Feder, Jennifer; Crainiceanu, Ciprian M.; Caffo, Brian; and Reich, Daniel, "PENALIZED FUNCTIONAL REGRESSION" (January 2010). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 204.
https://biostats.bepress.com/jhubiostat/paper204