Bibliographic record
Abstract
In this paper we consider a discretely sampled Jacobi process appropriate to specify the dynamics of a process with range [0,1], such as a discount coefficient, a regime probability, or a state price. The discrete time transition of the Jacobi process does not admit a closed form expression and therefore the exact maximum likelihood is unfeasable. We first review different characterizations of the transition function based on nonlinear canonical decomposition, partial differential equations.. They allow for approximations of the log-likelihood function which can be used to define an approximated maximum likelihood estimator. The finite sample properties of this estimator are compared with the properties of Kessler and Sorensen’s estimator based on the eigenfunctions of the generator of the diffusion. But also simulation based estimators, such as generalized method of moments (GMM) estimator, simulated method of moments (SMM) estimator or indirect inference estimator are considered. Résumé Dans cet essai, nous considérons un processus de Jacobi échantillonné en temps discret permettant de spécifier la dynamique de processus à valeurs dans [0, 1], tels qu ’ un coéfficient d’actualisation, une probabilité de régime, ou un prix d’état. La fonction de transition en temps discret du processus de Jacobi n’admettant pas ∗ University of Toronto † Centre interuniversitaire de recherche en économie quantitative (CIREQ), Centre interuniversitaire de recherche en analyse des organisations (CIRANO), and Département de sciences
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.253 | 0.109 |
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".