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August 2018 Semiparametric quantile estimation for varying coefficient partially linear measurement errors models
Jun Zhang, Yan Zhou, Xia Cui, Wangli Xu
Braz. J. Probab. Stat. 32(3): 616-656 (August 2018). DOI: 10.1214/17-BJPS357

Abstract

We study varying coefficient partially linear models when some linear covariates are error-prone, but their ancillary variables are available. After calibrating the error-prone covariates, we study quantile regression estimates for parametric coefficients and nonparametric varying coefficient functions, and we develop a semiparametric composite quantile estimation procedure. Asymptotic properties of the proposed estimators are established, and the estimators achieve their best convergence rate with proper bandwidth conditions. Simulation studies are conducted to evaluate the performance of the proposed method, and a real data set is analyzed as an illustration.

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Jun Zhang. Yan Zhou. Xia Cui. Wangli Xu. "Semiparametric quantile estimation for varying coefficient partially linear measurement errors models." Braz. J. Probab. Stat. 32 (3) 616 - 656, August 2018. https://doi.org/10.1214/17-BJPS357

Information

Received: 1 December 2016; Accepted: 1 February 2017; Published: August 2018
First available in Project Euclid: 8 June 2018

zbMATH: 06930042
MathSciNet: MR3812385
Digital Object Identifier: 10.1214/17-BJPS357

Rights: Copyright © 2018 Brazilian Statistical Association

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Vol.32 • No. 3 • August 2018
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