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Estimating spatial quantile regression with functional coefficients: a robust semiparametric framework

Estimating spatial quantile regression with functional coefficients: a robust semiparametric framework
Estimating spatial quantile regression with functional coefficients: a robust semiparametric framework
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
1350-7265
164-189
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Tang, Qingguo
8f11963a-7995-4a1d-a331-a58e94dffab3
Cheng, Longsheng
a1e09ded-e2e8-47d1-ae99-9345cf8b0967
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Tang, Qingguo
8f11963a-7995-4a1d-a331-a58e94dffab3
Cheng, Longsheng
a1e09ded-e2e8-47d1-ae99-9345cf8b0967

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

Record type: Article

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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Accepted/In Press date: September 2013
Published date: 2014
Organisations: Mathematical Sciences

Identifiers

Local EPrints ID: 356169
URI: https://eprints.soton.ac.uk/id/eprint/356169
ISSN: 1350-7265
PURE UUID: 5000f552-0a39-4f15-8b1d-9f0c11a460bb
ORCID for Zudi Lu: ORCID iD orcid.org/0000-0003-0893-832X

Catalogue record

Date deposited: 28 Aug 2013 11:48
Last modified: 29 Aug 2019 00:33

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Contributors

Author: Zudi Lu ORCID iD
Author: Qingguo Tang
Author: Longsheng Cheng

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