ETASbootstrap 0.2.0: A flexible R package for computing bootstrap confidence intervals for parameters in the space–time epidemic-type aftershock sequence model, with four case studies
Bibliographic record
Abstract
The space–time epidemic-type aftershock sequence (ETAS) model is a widely used tool for stochastic declustering of earthquake data catalogs and short-term aftershock forecasting. However, confidence intervals derived from asymptotic standard errors (ASEs) of parameter estimates based on maximum-likelihood theory can sometimes be misleading and it was recently suggested to use bootstrap confidence intervals instead Dutilleul et al. (2024). The ETASbootstrap package was developed to facilitate the use of the bootstrap resampling procedure and its associated confidence intervals for a direct comparison with asymptotic ones. In this paper, the statistical underpinnings of the package are first presented, including the space–time ETAS model with its multiple parameters, the importance of edge effects, and the bootstrapping algorithm. Then, three earthquake data catalogs (Japan, Italy, Iran) are used as input to ETASbootstrap 0.2.0, which is more flexible regarding the shape of spatial windows than the original version. In all cases, a discrepancy was observed between bootstrap and asymptotic confidence intervals for some of the space–time ETAS model parameters. It was possible to relate this discrepancy to the presence of outliers and a resulting lack of normality, which compromised the asymptotic approximation of the variability of the maximum-likelihood estimates (ML estimates). The results suggest that the two types of confidence intervals should be used in practice, especially for earthquake data catalogs of moderate size. • This package computes bootstrap confidence intervals for space–time ETAS model parameters. • A summary of the statistical methodology behind the package is presented. • Three earthquake data catalogs are used to illustrate the methodology. • Bootstrap confidence intervals are shown to be more reliable than asymptotic intervals.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".