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New method estimates isotropic covariance functions with improved accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new nonparametric model using Bernstein polynomials to approximate isotropic covariance functions in infinite-dimensional spaces. This method, termed sieve maximum likelihood (sML) estimation, offers a computationally efficient way to estimate these functions. Numerical comparisons indicate that the sML estimator outperforms existing parametric and nonparametric methods in reducing bias and achieving lower error norms, with a practical application demonstrated on precipitation data. AI

IMPACT Introduces a novel statistical estimation technique that could improve modeling accuracy in various data analysis applications.

RANK_REASON The cluster contains an academic paper published on arXiv detailing a new statistical methodology.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method estimates isotropic covariance functions with improved accuracy

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yiming Wang, Sujit K. Ghosh ·

    Nonparametric Estimation of Isotropic Covariance Function

    arXiv:2604.22320v1 Announce Type: cross Abstract: A nonparametric model using a sequence of Bernstein polynomials is constructed to approximate arbitrary isotropic covariance functions valid in $\mathbb{R}^\infty$ and related approximation properties are investigated using the po…

  2. arXiv stat.ML TIER_1 English(EN) · Sujit K. Ghosh ·

    Nonparametric Estimation of Isotropic Covariance Function

    A nonparametric model using a sequence of Bernstein polynomials is constructed to approximate arbitrary isotropic covariance functions valid in $\mathbb{R}^\infty$ and related approximation properties are investigated using the popular $L_{\infty}$ norm and $L_2$ norms. A computa…

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