Approximately linear INGARCH models for spatio-temporal counts
| العنوان: | Approximately linear INGARCH models for spatio-temporal counts |
|---|---|
| المؤلفون: | Malte Jahn, Christian H Weiß, Hee-Young Kim |
| المصدر: | Journal of the Royal Statistical Society Series C: Applied Statistics. 72:476-497 |
| بيانات النشر: | Oxford University Press (OUP), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Statistics and Probability, Statistics, Probability and Uncertainty |
| الوصف: | Existing integer-valued generalised autoregressive conditional heteroskedasticity (INGARCH) models for spatio-temporal counts do not allow for negative parameter and autocorrelation values. Using approximately linear INGARCH models, the unified and flexible spatio-temporal (B)INGARCH framework for modelling unbounded (bounded) counts is proposed. These models combine negative dependencies with kinds of a long memory. They are easily adapted to special marginal features or cross-dependencies: When modelling precipitation data (counts of rainy hours), we account for zero-inflation, while for cloud-coverage data (counts of okta), we deal with missing data and additional cross-correlation. A copula related to the spatial error model shows an appealing performance. |
| تدمد: | 1467-9876 0035-9254 |
| DOI: | 10.1093/jrsssc/qlad018 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::60f99abbbcd2957550613d020f9822c4 https://doi.org/10.1093/jrsssc/qlad018 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........60f99abbbcd2957550613d020f9822c4 |
| قاعدة البيانات: | OpenAIRE |
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