A spatial quantile regression model for driving mechanism of urban heat island by considering the spatial dependence and spatial non-stationary: An example of Beijing, China

2022 ◽  
pp. 103692
Author(s):  
Yuli Gu ◽  
Xue-yi You
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Urban Heat Island ◽  
Spatial Dependence ◽  
Driving Mechanism ◽  
Heat Island ◽  
Quantile Regression Model ◽  
Urban Heat ◽  
Beijing China

Characteristic change and Analysis of urban heat island in Beijing , China, Summer, 2003-2011

2014 ◽  
Vol 34 (15) ◽  
Author(s):  
阳文锐 YANG Wenrui ◽  
李锋 LI Feng ◽  
何永 HE Yong
Keyword(s):  
Urban Heat Island ◽  
Characteristic Change ◽  
Heat Island ◽  
Urban Heat ◽  
Beijing China

scholarly journals Choosing the Right Spatial Weighting Matrix in a Quantile Regression Model

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Philip Kostov
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Rate Of Convergence ◽  
Spatial Dependence ◽  
Spatial Models ◽  
Difficult Problem ◽  
Weighting Matrix ◽  
Quantile Regression Model ◽  
Regression Approach ◽  
The Right

This paper proposes computationally tractable methods for selecting the appropriate spatial weighting matrix in the context of a spatial quantile regression model. This selection is a notoriously difficult problem even in linear spatial models and is even more difficult in a quantile regression setup. The proposal is illustrated by an empirical example and manages to produce tractable models. One important feature of the proposed methodology is that by allowing different degrees and forms of spatial dependence across quantiles it further relaxes the usual quantile restriction attributable to the linear quantile regression. In this way we can obtain a more robust, with regard to potential functional misspecification, model, but nevertheless preserve the parametric rate of convergence and the established inferential apparatus associated with the linear quantile regression approach.

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