State-space models with GARCH errors: application to health data
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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 G Les modles d'espace d'tats sont utiliss pour tudier des donnes non stationnaires.Cependant, en prsence de termes d'erreur non gaussiens, le modle d'espace d'tats standard ne s'applique pas.Nous tudions les proprits des modles univaris et multivaris d'espacetat sous htroskdasticit conditionnelle et fractures structurelles multiples.Cela nous permet d'tendre les modles d'espace d'tats standard aux donnes queue lourde et de prendre en compte les paramtres dynamiques.Nous dveloppons un algorithme d'chantillonnage de Gibbs pour raliser l'infrence baysienne sur les paramtres et le vecteur d'tat latent.Enfin, nous menons une tude empirique sur les donnes de l'unit de soins intensifs.Nous constatons que nos modles sont mieux mme de rendre compte de la variation des donnes par rapport aux modles despace tats standard.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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