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Faster Uniform Convergence Rates For Deconvolution Estimators From Repeated Measurements

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  • Chen, Liang
  • Zhang, Minyuan

Abstract

Recently, Kurisu and Otsu (2022b, Econometric Theory 38(1), 172–193) derived the uniform convergence rates for the nonparametric deconvolution estimators proposed by Li and Vuong (1998, Journal of Multivariate Analysis 65(2), 139–165). This article shows that faster uniform convergence rates can be established for their estimators under the same assumptions. In addition, a new class of deconvolution estimators based on a variant of Kotlarski’s identity is also proposed. It is shown that in some cases, these new estimators can have faster uniform convergence rates than the existing estimators.

Suggested Citation

  • Chen, Liang & Zhang, Minyuan, 2025. "Faster Uniform Convergence Rates For Deconvolution Estimators From Repeated Measurements," Econometric Theory, Cambridge University Press, vol. 41(5), pages 1196-1228, October.
    • Handle: RePEc:cup:etheor:v:41:y:2025:i:5:p:1196-1228_6
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