| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳怡如 | |
| dc.creator (作者) | Chen, Vivian Yi-Ju;Li, Yi-Jin | |
| dc.date (日期) | 2026-04 | |
| dc.date.accessioned | 1-Apr-2026 16:37:06 (UTC+8) | - |
| dc.date.available | 1-Apr-2026 16:37:06 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Apr-2026 16:37:06 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=181853 | - |
| dc.description.abstract (摘要) | Geographically weighted regression (GWR) has been actively extended to accommodate count outcomes, yet existing approaches typically rely on restrictive distributional assumptions (e.g., Poisson, negative binomial) or two-part mixtures (e.g., zero-inflated models) that complicate estimation and interpretation. In this study, we propose a geographically weighted Poisson–Tweedie model (GWPTM), which integrates the Poisson–Tweedie distribution family into the GWR framework to provide a flexible approach for spatial count data analysis. By specifying variance as a power function of the mean, GWPTM unifies Poisson, negative binomial, and related count processes within a single-stage framework. This enables the model to naturally account for a broad spectrum of dispersion patterns as well as excess zeros and tail behavior, while allowing both regression coefficients and distributional parameters to vary across space. We develop an estimating function approach for local parameter estimation and inference. Simulation studies show that GWPTM accurately recovers spatially varying relationships, adapts effectively to heterogeneous dispersion patterns, and exhibits competitive performance against benchmark methods as well as favorable finite-sample behavior. An application to Taiwan dengue fever data further illustrates the practical advantages of GWPTM, which achieves superior explanatory and predictive performance and reveals pronounced spatial nonstationarity in both covariate effects and distributional characteristics that competing methods fail to capture. Overall, the proposed GWPTM offers a useful and parsimonious framework for analyzing spatially heterogeneous count data. | |
| dc.format.extent | 108 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Spatial Statistics, Vol.72, 100959 | |
| dc.subject (關鍵詞) | Geographically weighted regression; Spatial count data analysis; Poisson–Tweedie model; Overdispersion; Zero-inflation | |
| dc.title (題名) | Geographically weighted Poisson–Tweedie model for count data | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1016/j.spasta.2026.100959 | |
| dc.doi.uri (DOI) | https://doi.org/10.1016/j.spasta.2026.100959 | |
