Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data-Reference-Cited by-同舟云学术

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

Published:2012-04 Issue:2 Volume:44 Page:134-150
ISSN:0016-7363
Container-title:Geographical Analysis
language:en
Short-container-title:Geogr Anal

Author:

Chen Vivian Yi-Ju¹,Deng Wen-Shuenn¹,Yang Tse-Chuan²,Matthews Stephen A.³

Affiliation:

1. Department of Statistics; Tamkang University; Taipei; Taiwan

2. The Social Science Research Institute; The Pennsylvania State University; University Park; PA; USA

3. Department of Sociology & Department of Anthropology; The Pennsylvania State University; University Park; PA; USA

Publisher

Wiley

Subject

Earth-Surface Processes,Geography, Planning and Development

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/j.1538-4632.2012.00841.x/fullpdf

Reference57 articles.

1. Cross-Validation in Nonparametric Parametric Quantile Regression;Abberger;Allgemeines Statistisches Archiv,1998

2. Directional Rates of Change under Spatial Process Models;Banerjee;Journal of the American Statistical Association,2003

3. From Spatial Analysis to Geospatial Science;Berry;Geographical Analysis,2008

4. Local Statistical Spatial Analysis: Inventory and Prospect;Boots;International Journal of Geographical Information Science,2007

5. Logistic Quantile Regression for Bounded Outcomes;Bottai;Statistics in Medicine,2010

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4. Revealing the heterogeneous effects of new urbanization on urban-rural inequality using geographically weighted quantile regression;Applied Geography;2023-10

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