scholarly journals Exploring Heterogeneities with Geographically Weighted Quantile Regression: An Enhancement Based on the Bootstrap Approach

2020 ◽  
Vol 52 (4) ◽  
pp. 642-661
Author(s):  
Vivian Yi‐Ju Chen ◽  
Tse‐Chuan Yang ◽  
Stephen A. Matthews
Keyword(s):  
Quantile Regression ◽  
Bootstrap Approach ◽  
Weighted Quantile Regression

scholarly journals Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data

2012 ◽  
Vol 44 (2) ◽  
pp. 134-150 ◽  
Author(s):  
Vivian Yi-Ju Chen ◽  
Wen-Shuenn Deng ◽  
Tse-Chuan Yang ◽  
Stephen A. Matthews
Keyword(s):  
Quantile Regression ◽  
Mortality Data ◽  
Weighted Quantile Regression

Weighted quantile regression with nonelliptically structured covariates

2008 ◽  
Vol 36 (4) ◽  
pp. 595-611 ◽  
Author(s):  
Matías Salibián-Barrera ◽  
Ying Wei
Keyword(s):  
Quantile Regression ◽  
Weighted Quantile Regression

scholarly journals Global and Geographically Weighted Quantile Regression for Modeling the Incident Rate of Children’s Lead Poisoning in Syracuse, NY, USA

2018 ◽  
Vol 15 (10) ◽  
pp. 2300 ◽  
Author(s):  
Zhen Zhen ◽  
Qianqian Cao ◽  
Liyang Shao ◽  
Lianjun Zhang
Keyword(s):  
High Risk ◽  
Environmental Factors ◽  
Inner City ◽  
Quantile Regression ◽  
Lead Poisoning ◽  
Lead Concentration ◽  
Lead Level ◽  
Soil Lead ◽  
Incident Rate ◽  
Weighted Quantile Regression

Objective: The purpose of this study was to explore the full distribution of children’s lead poisoning and identify “high risk” locations or areas in the neighborhood of the inner city of Syracuse (NY, USA), using quantile regression models. Methods: Global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to model the relationships between children’s lead poisoning and three environmental factors at different quantiles (25th, 50th, 75th, and 90th). The response variable was the incident rate of children’s blood lead level ≥ 5 µg/dL in each census block, and the three predictor variables included building year, town taxable values, and soil lead concentration. Results: At each quantile, the regression coefficients of both global QR and GWQR models were (1) negative for both building year and town taxable values, indicating that the incident rate of children lead poisoning reduced with newer buildings and/or higher taxable values of the houses; and (2) positive for the soil lead concentration, implying that higher soil lead concentration around the house may cause higher risks of children’s lead poisoning. Further, these negative or positive relationships between children’s lead poisoning and three environmental factors became stronger for larger quantiles (i.e., higher risks). Conclusions: The GWQR models enabled us to explore the full distribution of children’s lead poisoning and identify “high risk” locations or areas in the neighborhood of the inner city of Syracuse, which would provide useful information to assist the government agencies to make better decisions on where and what the lead hazard treatment should focus on.

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