State-space models with GARCH errors: application to health data
Bibliographic record
Abstract
State-space models are used to study non-stationary data.However, in the presence on non-Gaussian error terms the standard state-space model does not apply.We investigate the properties of univariate and multivariate state-space models under conditional heteroskedasticity and multiple structural breaks.This allows us to extend the standard state-space models to heavy tailed data and allow for dynamic parameters.We develop a Gibbs sampling algorithm to carry out Bayesian inference on the parameters and the latent state vector.Finally, we carry out an empirical study on ICU data.We find that our models are better able to capture the variation in the data than the standard state-space models.iii ABR ÉG É Les modèles d'espace d'ètats sont utilisés pour étudier des données non stationnaires.Cependant, en présence de termes d'erreur non gaussiens, le modèle d'espace d'états standard ne s'applique pas.Nous étudions les propriétés des modèles univariés et multivariés d'espaceétat sous hétéroskédasticité conditionnelle et fractures structurelles multiples.Cela nous permet d'étendre les modèles d'espace d'états standard aux données à queue lourde et de prendre en compte les paramètres dynamiques.Nous développons un algorithme d'échantillonnage de Gibbs pour réaliser l'inférence bayésienne sur les paramètres et le vecteur d'état latent.Enfin, nous menons une étude empirique sur les données de l'unité de soins intensifs.Nous constatons que nos modèles sont mieux à même de rendre compte de la variation des données par rapport aux modèles despace à états standard.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".