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- W2336152110 abstract "When studying associations between a functional covariate and scalar response using a functional linear model (FLM), scientific knowledge may indicate possible monotonicity of the unknown parameter curve. In this context, we propose an F-type test of monotonicity, based on a full versus reduced nested model structure, where the reduced model with monotonically constrained parameter curve is nested within an unconstrained FLM. For estimation under the unconstrained FLM, we consider two approaches: penalised least-squares and linear mixed model effects estimation. We use a smooth then monotonise approach to estimate the reduced model, within the null space of monotone parameter curves. A bootstrap procedure is used to simulate the null distribution of the test statistic. We present a simulation study of the power of the proposed test, and illustrate the test using data from a head and neck cancer study." @default.
- W2336152110 created "2016-06-24" @default.
- W2336152110 creator A5028340128 @default.
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- W2336152110 date "2016-04-02" @default.
- W2336152110 modified "2023-09-24" @default.
- W2336152110 title "An F-type test for detecting departure from monotonicity in a functional linear model" @default.
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- W2336152110 doi "https://doi.org/10.1080/10485252.2016.1163352" @default.
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