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- W2897615392 abstract "Functional linear model is of great practical importance, as exemplified by applications in high-throughput studies such as meteorological and biomedical research. In this paper, we propose a new functional variable selection procedure, called functional variable selection via Gram–Schmidt (FGS) orthogonalization, for a functional linear model with a scalar response and multiple functional predictors. Instead of the regularization methods, FGS takes into account the similarity between the functional predictors in a data-driven way and utilizes the technique of Gram–Schmidt orthogonalization to remove the irrelevant predictors. FGS can successfully discriminate between the relevant and the irrelevant functional predictors to achieve a high true positive ratio without including many irrelevant predictors, and yield explainable models, which offers a new perspective for the variable selection method in the functional linear model. Simulation studies are carried out to evaluate the finite sample performance of the proposed method, and also a weather data set is analysed." @default.
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- W2897615392 date "2018-10-08" @default.
- W2897615392 modified "2023-10-16" @default.
- W2897615392 title "Functional variable selection via Gram–Schmidt orthogonalization for multiple functional linear regression" @default.
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- W2897615392 doi "https://doi.org/10.1080/00949655.2018.1530776" @default.
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