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- W2963804900 abstract "This paper develops a simple method to construct confidence bands, centered at a principal component analysis (PCA) based estimator, for the slope function in a functional linear regression model with a scalar response variable and a functional predictor variable. The PCA-based estimator is a series estimator with estimated basis functions, and so construction of valid confidence bands for it is a non-trivial challenge. We propose a confidence band that aims at covering the slope function at most of points with a prespecified probability (level), and prove its asymptotic validity under suitable regularity conditions. Importantly, this is the first paper that derives confidence bands having theoretical justifications for the PCA-based estimator. We also propose a practical method to choose the cut-off level used in PCA-based estimation, and conduct numerical studies to verify the finite sample performance of the proposed confidence band. Finally, we apply our methodology to spectrometric data, and discuss extensions of our methodology to cases where additional vector-valued regressors are present." @default.
- W2963804900 created "2019-07-30" @default.
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- W2963804900 date "2019-01-01" @default.
- W2963804900 modified "2023-09-24" @default.
- W2963804900 title "A simple method to construct confidence bands in functional linear regression" @default.
- W2963804900 cites W2059173978 @default.
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- W2963804900 doi "https://doi.org/10.5705/ss.202017.0208" @default.
- W2963804900 hasPublicationYear "2019" @default.
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