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Dependence of variance on covariate design in nonparametric link regression

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  • Okuno, Akifumi
  • Yano, Keisuke

Abstract

This paper discusses a design-dependent nature of variance in nonparametric link regression aiming at predicting a mean outcome at a link, i.e., a pair of nodes, based on currently observed data comprising covariates at nodes and outcomes at links.

Suggested Citation

  • Okuno, Akifumi & Yano, Keisuke, 2023. "Dependence of variance on covariate design in nonparametric link regression," Statistics & Probability Letters, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:stapro:v:193:y:2023:i:c:s0167715222002292
    DOI: 10.1016/j.spl.2022.109716
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    References listed on IDEAS

    as
    1. Bryan S. Graham & Fengshi Niu & James L. Powell, 2020. "Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression," Papers 2012.08444, arXiv.org, revised Mar 2021.
    2. Yiwei He & Yingjie Tian & Jingjing Tang & Yue Ma, 2018. "Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization," Complexity, Hindawi, vol. 2018, pages 1-13, April.
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    More about this item

    Keywords

    Similarity learning; Link prediction;

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