-д хадгалсан:
| Үндсэн зохиолч: | |
|---|---|
| Формат: | Preprint |
| Хэвлэсэн: |
2025
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://arxiv.org/abs/2502.09255 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
|
Агуулга:
- Analyzing demographic data collected across multiple populations, time periods, and age groups is challenging due to the interplay of high dimensionality, demographic heterogeneity among groups, and stochastic variability within smaller groups. This paper proposes a Bayesian matrix factor model to address these challenges. By factorizing count data matrices as the product of low-dimensional latent age and time factors, the model achieves a parsimonious representation that mitigates overfitting and remains computationally feasible even when hundreds of populations are involved. Informative priors enforce smoothness in the age factors and allow for the dynamic evolution of the time factors. A straightforward Markov chain Monte Carlo algorithm is developed for posterior inference. Applying the model to Austrian district-level migration data from 2002 to 2023 demonstrates its ability to accurately reconstruct complex demographic processes using only a fraction of the parameters required by conventional demographic factor models. A forecasting exercise shows that the proposed model consistently outperforms standard benchmarks. Beyond statistical demography, the framework holds promise for a wide range of applications involving noisy, heterogeneous, and high-dimensional non-Gaussian matrix-valued data.