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Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data
Vivian Yi-Ju Chen1, Wen-Shuenn Deng1, Tse-Chuan Yang2
1Department of Statistics, Tamkang University, Tamsui, Taipei 251, Taiwan.
Geographical Analysis
|October 25, 2014
Summary
Geographically Weighted Quantile Regression (GWQR) combines spatial analysis and distribution modeling. This method reveals how social determinants impact mortality rates across different spatial locations and distribution percentiles.
Area of Science:
- Spatial statistics
- Econometrics
- Geographical analysis
Background:
- Geographically Weighted Regression (GWR) addresses spatial non-stationarity.
- Quantile Regression (QR) models the entire distribution of a dependent variable.
- Combining GWR and QR has been underexplored.
Purpose of the Study:
- Introduce Geographically Weighted Quantile Regression (GWQR).
- Combine spatial non-stationarity and distribution modeling.
- Analyze spatial variations in mortality determinants.
Main Methods:
- Review GWR and QR methodologies.
- Outline the GWQR approach, including parameter estimation and standard errors.
- Discuss bandwidth selection via cross-validation and non-stationarity testing.
Main Results:
- Applied GWQR to U.S. county data on mortality and social determinants.
- Demonstrated that associations between mortality and determinants vary spatially.
- Showed these associations also vary across the mortality distribution (5th to 95th percentiles).
Conclusions:
- GWQR effectively bridges spatial and distribution-based statistical analyses.
- Findings offer new insights into mortality determinants and their spatial-distributional complexities.
- Results are relevant for public policy and health promotion strategies.
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