arXiv Machine Learning
Optimal estimation for Functional Linear Regression with Noisy Discretized Data
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Aug 27
Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality
The paper introduces a pooling‑ridge estimation method for functional linear regression that handles data observed at discrete times, ranging from sparse to dense designs. By combining pooling strategies with RKHS‑based techniques, the authors achieve minimax‑optimal prediction risk for both scalar‑on‑function and function‑on‑function models. The study identifies distinct phase transitions in convergence behavior, with up to three transitions for function‑on‑function regression, and validates the approach through simulations and real data examples.
arXiv Machine Learning
Jul 13
Near-optimal Delta-convex Estimation of Lipschitz Functions
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Conditional regression for the Nonlinear Single-Variable Model
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Sufficient Dimesion Reduction via Generalized Stein's Lemma
arXiv:2608. 15121v1 Announce Type: cross Abstract: Sufficient dimension reduction (SDR) seeks the minimal subspace of the predictors that captures the full conditional distribution of the response, which is known as the central subspace (CS).
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Locally Adaptive Conformal Inference for Operator Models
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Scalable estimation of VARMA models
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