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- W4288333435 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." @default.
- W4288333435 created "2022-07-28" @default.
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- W4288333435 date "2019-06-11" @default.
- W4288333435 modified "2023-09-27" @default.
- W4288333435 title "Model Testing for Generalized Scalar-on-Function Linear Models" @default.
- W4288333435 doi "https://doi.org/10.48550/arxiv.1906.04889" @default.
- W4288333435 hasPublicationYear "2019" @default.
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