Year: 2019
Type: preprint
Abstract: Scalar-on-function linear models are commonly used to regress functional predictors on a scalar response. However, functional models are more difficult to estimate and interpret than traditional linear models, and may be unnecessarily complex for a data application. Hypothesis testing can be used to guide model selection by determining if a functional predictor is necessary. Using a mixed effects representation with penalized splines and variance component tests, we propose a framework for testing functional linear models with responses from exponential family distributions. The proposed method can accommodate dense and sparse functional data, and be used to test functional predictors for no effect and form of the effect. We show via simulation study that the proposed method achieves the nominal level and has high power, and we demonstrate its utility with two data applications.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G4387489117, G5911562, CAREER: Next Generation Functional Methods for the Analysis of Emerging Repeated Measurements, G8233791481, North Carolina State University - to help the foundation develop data-driven strategies for delivering the right intervention(s) to the right child, at the right time, and at the ri...
OpenAlex ID: W2952208647