Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2511.04331 |
| Tags: |
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Inhaltsangabe:
- This paper introduces a matrix-variate regression model for analyzing multivariate data observed across spatial locations and over time. The model's design incorporates a mean structure that links covariates to the response matrix and a separable covariance structure, based on a Kronecker product, to capture spatial and temporal dependencies efficiently. We derive maximum likelihood estimators for all model parameters. A simulation study validates the model, showing its effectiveness in parameter recovery across different spatial resolutions. Finally, an application to real-world data on agricultural and livestock production from Brazilian municipalities showcases the model's practical utility in revealing structured spatio-temporal patterns of variation and covariate effects.