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Exponentially weighted averaging of varying-coefficient partially linear models

Author

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  • Bosen Cui
  • Jialiang Li
  • David Nott

Abstract

Despite the increasing attention given to frequentist model averaging methods, few studies address the theoretical aspects of semiparametric model averaging. In this paper, we introduce an exponentially weighted model averaging (EWA) method for the varying-coefficient partially linear model (VCPLM). Our weighting method is straightforward to implement and has a natural Bayesian interpretation. We establish oracle inequalities for the finite sample error bound of the empirical estimators and thoroughly investigate the asymptotic optimality of the proposed weighting criterion, along with other statistical properties. Some numerical studies are provided which support the theoretical findings.

Suggested Citation

  • Bosen Cui & Jialiang Li & David Nott, 2026. "Exponentially weighted averaging of varying-coefficient partially linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 38(2), pages 377-397, April.
    • Handle: RePEc:taf:gnstxx:v:38:y:2026:i:2:p:377-397
    DOI: 10.1080/10485252.2025.2464796
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