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February 2014 Estimating spatial quantile regression with functional coefficients: A robust semiparametric framework
Zudi Lu, Qingguo Tang, Longsheng Cheng
Bernoulli 20(1): 164-189 (February 2014). DOI: 10.3150/12-BEJ480

Abstract

This paper considers an estimation of semiparametric functional (varying)-coefficient quantile regression with spatial data. A general robust framework is developed that treats quantile regression for spatial data in a natural semiparametric way. The local M-estimators of the unknown functional-coefficient functions are proposed by using local linear approximation, and their asymptotic distributions are then established under weak spatial mixing conditions allowing the data processes to be either stationary or nonstationary with spatial trends. Application to a soil data set is demonstrated with interesting findings that go beyond traditional analysis.

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Zudi Lu. Qingguo Tang. Longsheng Cheng. "Estimating spatial quantile regression with functional coefficients: A robust semiparametric framework." Bernoulli 20 (1) 164 - 189, February 2014. https://doi.org/10.3150/12-BEJ480

Information

Published: February 2014
First available in Project Euclid: 22 January 2014

zbMATH: 06282546
MathSciNet: MR3160577
Digital Object Identifier: 10.3150/12-BEJ480

Rights: Copyright © 2014 Bernoulli Society for Mathematical Statistics and Probability

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Vol.20 • No. 1 • February 2014
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