Abstract:The existing methods for functional regression can be roughly divided into two categories: direct functional regression (DFR) and functional regression based on functional principal component analysis (FR-FPCA). DFR may contain too much noise, while FR-FPCA may be inefficient because FPCA is independent of the response. In this paper, we investigate the effect of a vector of random curves on a response by extracting the latent features of the random curves that are associated with the response. Furthermore, to… Show more
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