Modeling nonlinear time series with local mixtures of generalized linear models
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Bibliographic record
Abstract
The authors consider a novel class of nonlinear time series models based on local mixtures of regressions of exponential family models, where the covariates include functions of lags of the dependent variable. They give conditions to guarantee consistency of the maximum likelihood estimator for correctly specified models, with stationary and nonstationary predictors. They show that consistency of the maximum likelihood estimator still holds under model misspecification. They also provide probabilistic results for the proposed model when the vector of predictors contains only lags of transformations of the modeled time series. They illustrate the consistency of the maximum likelihood estimator and the probabilistic properties via Monte Carlo simulations. Finally, they present an application using real data. Modélisation de séries chronologiques non linéaires l'aide de mélanges locaux de modèles linéaires généralisés: Les auteurs étudient une nouvelle classe de modèles non linéaires pour séries chronologiques construits à partir de mélanges locaux de régressions de modèles à base de familles exponentielles dans lesquels certaines fonctions des délais associés à la variable dépendante sont inclus à titre de covariables. Ils énoncent des conditions garantissant la convergence de l'estimateur du maximum de vraisemblance (EMV) pour des modèles bien spécifiés, avec prédicteurs stationnaires et non stationnaires. Ils montrent que l'EMV reste convergent même si le modèle est mal spécifié. Ils décrivent en outre le comportement probabiliste du modèle proposé lorsque le vecteur des prédicteurs se limite à certains dèlais de transformations de la série modélisée. Ils illustrent la convergence de l'EMV et le comportement probabiliste du modèle par le biais d'une étude de Monte-Carlo. Enfin, ils présentent une application sur des données delles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it