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. © 2014 ISI/BS.
| Original language | English |
|---|---|
| Pages (from-to) | 164-189 |
| Journal | Bernoulli |
| Volume | 20 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2014 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Asymptotic distributions
- Functional (varying) coefficient spatial regression
- Local M-estimators
- Quantile regression
- Robust framework
- Soil data analysis
- Spatial data
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