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Kernel Estimation When Density May Not Exist: A Corrigendum

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  • Zinde-Walsh, Victoria

Abstract

The paper “Kernel estimation when density may not exist” (Zinde-Walsh, 2008) considered density as a generalized function given by a functional on a space of smooth functions; this made it possible to establish the limit properties of the kernel estimator without assuming the existence of the density function. This note corrects an error in that paper in the derivation of the variance of the kernel estimator. The corrected result is that in the space of generalized functions the parametric rate of convergence of the kernel density estimator to the limit Gaussian process is achievable.

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  • Zinde-Walsh, Victoria, 2017. "Kernel Estimation When Density May Not Exist: A Corrigendum," Econometric Theory, Cambridge University Press, vol. 33(5), pages 1259-1263, October.
    • Handle: RePEc:cup:etheor:v:33:y:2017:i:05:p:1259-1263_00
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    Cited by:

    1. Yulia Kotlyarova & Marcia M. A. Schafgans & Victoria Zinde-Walsh, 2021. "Rates of Expansions for Functional Estimators," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 121-139, December.
    2. Meho Saša Kovačević & Lovorka Librić & Gordana Ivoš & Anita Cerić, 2020. "Application of Reliability Analysis for Risk Ranking in a Levee Reconstruction Project," Sustainability, MDPI, vol. 12(4), pages 1-17, February.

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