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TitleGeographically weighted quantile regression for count Data
Creator陳怡如
Chen, Vivian Yi-Ju;Wang, Shi-Ting
Contributor統計系
Key WordsCount data; Spatial nonstationarity; Quantile regression; Geographically weighted regression
Date2025-01
Date Issued14-Apr-2025 09:24:12 (UTC+8)
SummaryIn recent years, a methodological framework known as geographically weighted quantile regression (GWQR) has emerged for spatial data analysis. This framework offers the abilities to simultaneously explore spatial heterogeneity or nonstationarity in regression relationships and to estimate various conditional quantile functions. However, the current configuration of GWQR is limited to the analysis of continuous dependent variables. Discrete count data are observed in many disciplines. Whenever modeling such outcomes is necessary, the conventional GWQR approach is inadequate and fails to provide comprehensive insights into count data. To address this issue, this study aims to extend the GWQR framework originally designed for continuous dependent variables to accommodate count outcomes. We introduce an approach called geographically weighted count quantile regression (GWCQR), wherein the model specification is based on the smoothing of count responses through a jittering procedure. A semiparametric counterpart that allows for the inclusion of both spatially varying and invariant coefficients is also discussed. Finally, the proposed techniques are applied to a dataset of dengue fever in Taiwan as an empirical illustration.
RelationStatistics and Computing, Vol.35, article number: 33
Typearticle
DOI https://doi.org/10.1007/s11222-025-10568-z
dc.contributor 統計系
dc.creator (作者) 陳怡如
dc.creator (作者) Chen, Vivian Yi-Ju;Wang, Shi-Ting
dc.date (日期) 2025-01
dc.date.accessioned 14-Apr-2025 09:24:12 (UTC+8)-
dc.date.available 14-Apr-2025 09:24:12 (UTC+8)-
dc.date.issued (上傳時間) 14-Apr-2025 09:24:12 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/156523-
dc.description.abstract (摘要) In recent years, a methodological framework known as geographically weighted quantile regression (GWQR) has emerged for spatial data analysis. This framework offers the abilities to simultaneously explore spatial heterogeneity or nonstationarity in regression relationships and to estimate various conditional quantile functions. However, the current configuration of GWQR is limited to the analysis of continuous dependent variables. Discrete count data are observed in many disciplines. Whenever modeling such outcomes is necessary, the conventional GWQR approach is inadequate and fails to provide comprehensive insights into count data. To address this issue, this study aims to extend the GWQR framework originally designed for continuous dependent variables to accommodate count outcomes. We introduce an approach called geographically weighted count quantile regression (GWCQR), wherein the model specification is based on the smoothing of count responses through a jittering procedure. A semiparametric counterpart that allows for the inclusion of both spatially varying and invariant coefficients is also discussed. Finally, the proposed techniques are applied to a dataset of dengue fever in Taiwan as an empirical illustration.
dc.format.extent 106 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Statistics and Computing, Vol.35, article number: 33
dc.subject (關鍵詞) Count data; Spatial nonstationarity; Quantile regression; Geographically weighted regression
dc.title (題名) Geographically weighted quantile regression for count Data
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1007/s11222-025-10568-z
dc.doi.uri (DOI) https://doi.org/10.1007/s11222-025-10568-z