A heavy-tailed model for multivariate spatial processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental data commonly involves measuring multiple pollutants, such as NO 2 and PM 10 levels, at some fixed sites across a region. Data analysts aim to describe the processes accounting for covariance across space and among pollutants, usually assuming a multivariate spatial Gaussian model with a stationary covariance function. However, the observed data distribution often exhibits heterogeneous variability, resulting in heavier tails than the Gaussian distribution. To address these challenges by avoiding data transformation, we propose a flexible multivariate spatial model with spatially varying covariate-dependent variance that naturally accommodates heavy-tailed distributions. Specifically, we extend the linear model of coregionalization by modeling the variances of the processes, allowing them to vary across space and depending on covariates. We discuss the properties of the proposed model and outline a Bayesian inference procedure implemented using the software Stan . As the model involves several Gaussian process components, we further discuss Vecchia-based approximation methods for analyzing large spatial datasets. Artificial data analyses suggest that the model’s parameters are identifiable and can accurately detect outlying observations if they exist, underscoring the model’s reliability and robustness. The model quantifies uncertainty and captures local structures more effectively than the multivariate Gaussian model when applied to maximum concentrations of NO 2 and PM 10 on a day at 382 sites across Italy. Further, the described approximation methods show effectiveness in analyzing large spatial datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it