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FASTER UNIFORM CONVERGENCE RATES FOR DECONVOLUTION ESTIMATORS FROM REPEATED MEASUREMENTS

Published online by Cambridge University Press:  27 December 2024

Liang Chen and
Minyuan Zhang
Liang Chen*
Affiliation:
HSBC Business School, Peking University
Minyuan Zhang
Affiliation:
School of Economics, Shanghai University of Finance and Economics
*
Address correspondence to Liang Chen, HSBC Business School, Peking University, No. 2199 Lishui Road, Shenzhen, Guangdong 518055, China; e-mail: chenliang@phbs.pku.edu.cn
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Abstract

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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.

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Type
ARTICLES
Information
Econometric Theory , Volume 41 , Issue 5 , October 2025, pp. 1196 - 1228
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Footnotes

We are indebted to Simon Lee and two anonymous referees for their constructive inputs that have greatly improved the paper. Financial support from the National Natural Science Foundation of China (Grant No. 72473004) is gratefully acknowledged.

References

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