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Regression analysis of networked data

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  • Yan Zhou
  • Peter X.-K. Song

Abstract

This paper concerns regression methodology for assessing relationships between multi-dimensional response variables and covariates that are correlated within a network. To address analytical challenges associated with the integration of network topology into the regression analysis, we propose a hybrid quadratic inference method that uses both prior and data-driven correlations among network nodes. A Godambe information-based tuning strategy is developed to allocate weights between the prior and data-driven network structures, so the estimator is efficient. The proposed method is conceptually simple and computationally fast, and has appealing large-sample properties. It is evaluated by simulation, and its application is illustrated using neuroimaging data from an association study of the effects of iron deficiency on auditory recognition memory in infants.

Suggested Citation

  • Yan Zhou & Peter X.-K. Song, 2016. "Regression analysis of networked data," Biometrika, Biometrika Trust, vol. 103(2), pages 287-301.
    • Handle: RePEc:oup:biomet:v:103:y:2016:i:2:p:287-301.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw003
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    Cited by:

    1. Yevgeny V. POPOV, 2018. "Economic Sociotronics of the 21st Century," Upravlenets, Ural State University of Economics, vol. 9(2), pages 2-5, April.
    2. Oyarzo, Mauricio & Paredes, Dusan, 2021. "The impact of mining taxes on public education: Evidence for mining municipalities in Chile," Resources Policy, Elsevier, vol. 70(C).
    3. Shan, Liang & Kim, Inyoung, 2018. "Joint estimation of multiple Gaussian graphical models across unbalanced classes," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 89-103.

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