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- W4385261726 abstract "Functional sliced inverse regression (FSIR) is one of the most popular algorithms for functional sufficient dimension reduction (FSDR). However, the choice of slice scheme in FSIR is critical but challenging. In this paper, we propose a new method called functional slicing-free inverse regression (FSFIR) to estimate the central subspace in FSDR. FSFIR is based on the martingale difference divergence operator, which is a novel metric introduced to characterize the conditional mean independence of a functional predictor on a multivariate response. We also provide a specific convergence rate for the FSFIR estimator. Compared with existing functional sliced inverse regression methods, FSFIR does not require the selection of a slice number. Simulations demonstrate the efficiency and convenience of FSFIR." @default.
- W4385261726 created "2023-07-26" @default.
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- W4385261726 date "2023-07-24" @default.
- W4385261726 modified "2023-10-18" @default.
- W4385261726 title "Functional Slicing-free Inverse Regression via Martingale Difference Divergence Operator" @default.
- W4385261726 doi "https://doi.org/10.48550/arxiv.2307.12537" @default.
- W4385261726 hasPublicationYear "2023" @default.
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