Brazilian Journal of Probability and Statistics

Semiparametric quantile estimation for varying coefficient partially linear measurement errors models

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.

Article information

Source
Braz. J. Probab. Stat., Volume 32, Number 3 (2018), 616-656.

Dates
Accepted: February 2017
First available in Project Euclid: 8 June 2018

https://projecteuclid.org/euclid.bjps/1528444875

Digital Object Identifier
doi:10.1214/17-BJPS357

Mathematical Reviews number (MathSciNet)
MR3812385

Zentralblatt MATH identifier
06930042

Citation

Zhang, Jun; Zhou, Yan; Cui, Xia; Xu, Wangli. Semiparametric quantile estimation for varying coefficient partially linear measurement errors models. Braz. J. Probab. Stat. 32 (2018), no. 3, 616--656. doi:10.1214/17-BJPS357. https://projecteuclid.org/euclid.bjps/1528444875

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