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New estimator tackles nonlinear single-variable regression challenges

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new nonparametric estimator for nonlinear single-variable models, addressing the challenge of dimensionality in regression tasks. The method, based on response slicing and local principal component analysis, can achieve near-minimax-optimal rates under specific conditions related to the curve's variation and noise levels. The construction time is efficient, scaling polynomially with the ambient dimension. AI

RANK_REASON The cluster contains a single academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New estimator tackles nonlinear single-variable regression challenges

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The cluster contains a single academic paper detailing a new statistical method. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yantao Wu, Mauro Maggioni ·

    Conditional regression for the Nonlinear Single-Variable Model

    arXiv:2411.09686v4 Announce Type: replace Abstract: Regressing a function $F$ on $\mathbb{R}^d$ without incurring the statistical and computational curse of dimensionality requires exploitable structure. Compositional models $F=f\circ g$ in which $g$ has a low-dimensional range i…

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