Particle filtering for Gumbel‐distributed daily maxima of methane and nitrous oxide
Bibliographic record
Abstract
In atmospheric chemistry, daily maxima concentrations capture information about the variability among peak values. Statistically, they can often be modeled by a Gumbel distribution. This is the case for two very important greenhouse gases methane and nitrous oxide maxima when they are measured at our site of interest, Gif‐sur‐Yvette, a city south west of Paris. In practice, those two daily concentrations are not always recorded during the same period, and it would be of interest to reconstruct one from the other one. Such a type of inference can be handled within a state space modeling framework, but state space models are not tailored to represent the dynamics among Gumbel‐distributed maxima. By building on our previous work, which made a link between linear autoregressive time series and Gumbel‐distributed maxima, we propose and study such a state space model. It has the advantages of being linear and of preserving the Gumbel characteristic in both the state and observational equations. Concerning the inference of the hidden maxima at the state equation level, we derive the optimal weights of the auxiliary particle filtering approach of Pitt and Shephard. A simulation study indicates that our approach offers a gain over the Kalman filter, the bootstrap filter, and the nonmodified version of the Pitt and Shephard auxiliary filter. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".