Goodness‐of‐fit tests for copula‐based spatial models
Bibliographic record
Abstract
There has been a growing interest recently for the modeling of spatial data using multivariate copulas. Such an approach allows for the modeling of spatial dependence independently of the marginal distributions at each site and enables for spatial structures that go beyond the extensively used Gaussian random field. In this context, the choice of an appropriate family of copulas for a given spatial dataset is a crucial issue, in particular when one is interested in accurate spatial interpolations. This paper develops and investigates formal goodness‐of‐fit methodologies for spatial copula models when only one replicate of an isotropic random field is available at a finite number of sites; this setup is standard in geostatistics. Because of the limited information that is available, it is suggested that groups of random pairs sharing similar lag distances be created and that traditional goodness‐of‐fit statistics for bivariate copula families be computed for each group. These statistics are then combined into a global test statistic whosepvalue is approximated from a suitably adapted parametric bootstrap. The performance of the proposed tests in terms of size and power is investigated in an extensive simulation study. The newly introduced tools are then illustrated on zinc concentration measurements near the Meuse river and on snowfall data in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".