Hybrid conditional autoregressive generalized random forest for overdispersed spatial counts: Evidence from Kalimantan wildfire hotspots
Abstract
This paper proposes a negative binomial conditional autoregressive generalized random forest model for overdispersed spatial count data. The framework combines a flexible nonparametric mean function estimated via generalized random forests with conditional autoregressive spatial random effects under a negative binomial likelihood. We evaluate the proposed method through a simulation study and an empirical application to satellitederived wildfire hotspot counts in Kalimantan, Indonesia (September 2024), using rainfall, air temperature, relative humidity, wind speed, and the number of rainless days as covariates. Across simulation scenarios, negative binomial conditional autoregressive generalized random forest yields lower prediction errors than the benchmark negative binomial conditional autoregressive random forest. In the Kalimantan application, the model achieves a root mean squared error of 3.64 and a mean absolute error of 2.11 hotspots per grid cell. Variable importance analysis indicates that air temperature is the most influential predictor of the spatial distribution of hotspots.
Keywords
Conditional autoregressive generalized random forest overdispersed count data negative binomial spatial modelling
Supporting Institution
This study was financially supported by the Indonesian Education Scholarship [BPI ID: 202209090500], Center for Higher Education Funding and Assessment, Ministry of Higher Education, Science, and Technology of the Republic of Indonesia, and Endowment Fund for Education Agency, Ministry of Finance of the Republic of Indonesia.
References
- [1] H.A. Abood, S. Khatan Ismail and A.F.T. Sabah, Re-weighted least squares: the best negative binomial regression methods in determining the congenital anomalies’ risk factors, Syst. Rev. Pharm. 11 (12), 1956-1963, 2020.
Abstract
References
- [1] H.A. Abood, S. Khatan Ismail and A.F.T. Sabah, Re-weighted least squares: the best negative binomial regression methods in determining the congenital anomalies’ risk factors, Syst. Rev. Pharm. 11 (12), 1956-1963, 2020.
Details
| Primary Language | English |
|---|---|
| Subjects | Statistical Data Science, Spatial Statistics |
| Journal Section | Research Article |
| Authors | |
| Submission Date | December 30, 2025 |
| Acceptance Date | February 21, 2026 |
| Early Pub Date | March 9, 2026 |
| Publication Date | March 9, 2026 |
| DOI | https://doi.org/10.15672/hujms.1849711 |
| IZ | https://izlik.org/JA55NF93FS |
| Published in Issue | Year 2026 Issue: Advanced Online Publication |
Cite
Aim & Scope
Aim
Hacettepe Journal of Mathematics and Statistics (Formerly: Hacettepe Bulletin of Natural Sciences and Engineering - Series B) is an international, peer-reviewed, open-access journal that publishes high-quality original research articles and occasional comprehensive surveys in all areas of Mathematics and Statistics. The journal is committed to promoting the advancement of mathematical and statistical sciences by providing a platform for researchers to disseminate their findings to a global audience.
As an open-access journal, Hacettepe Journal of Mathematics and Statistics (HJMS) does not charge any article processing or publishing fees, ensuring that high-quality research remains freely accessible to the scientific community. The journal accepts submissions exclusively in English and upholds a rigorous peer-review process to maintain academic excellence.
The journal is widely recognized in the academic community and is indexed in leading databases such as Web of Science (Science Citation Index Expanded), Scopus, and Zentralblatt MATH. With its long-standing history and commitment to scholarly publishing, Hacettepe Journal of Mathematics and Statistics continues to serve as a valuable resource for researchers, academicians, and practitioners in the fields of Mathematics and Statistics.
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