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Exploring heterogeneities with geographically weighted quantile regression: An enhancement based on the bootstrap
Vivian Yi-Ju Chen1, Tse-Chuan Yang2, Stephen A Matthews3
1Department of Statistics, Tamkang University, Taipei, Taiwan.
Geographical Analysis
|April 23, 2021
Summary
Geographically weighted quantile regression (GWQR) inference is improved using a novel bootstrap approach. This method enhances GWQR
Area of Science:
- Spatial statistics
- Econometrics
- Geographical analysis
Background:
- Geographically weighted quantile regression (GWQR) analyzes spatial and response heterogeneity.
- Current GWQR inference relies on asymptotic approximations, posing challenges for finite samples.
Purpose of the Study:
- To introduce a bootstrap approach to overcome limitations in GWQR inference.
- To enhance the practical application and reliability of GWQR.
Main Methods:
- Developed and implemented a bootstrap methodology for GWQR.
- Validated the approach through simulation experiments.
- Applied the bootstrap method to US mortality data.
Main Results:
- The bootstrap approach provides a practical and reliable alternative for GWQR inference.
- Simulation results demonstrate the effectiveness of the bootstrap method.
- Empirical analysis confirms the enhanced utilization of GWQR with the bootstrap.
Conclusions:
- The proposed bootstrap method effectively addresses the limitations of asymptotic inference in GWQR.
- This enhancement improves the accuracy and applicability of GWQR for spatial data analysis.
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