A Bayesian Analysis of the Threshold ARMA Model
Bibliographic record
Abstract
In this paper we consider a Bayesian analysis for the threshold autoregressive moving average model with k regimes and orders (p ` ; q ` ) ; ` = 1; : : : ; k, OE p ` (B)X t = OE `;0 + ` q ` (B)a t ; if X t\\Gammad 2 ! ` ; where OE p ` (B) = 1 \\Gamma OE `;1 B \\Gamma : : : \\Gamma OE `;p ` B p ` ; ` q ` (B) = 1 + ` `;1 B + : : : + ` `;q ` B q ` ]; ; ` = 1; : : : ; k and the a t are iid N(0; ø \\Gamma1 ) ,the precision ø supposed to be common to all the regimes, ! ` forming a partition of the real line; d is the delay parameter. For the special case k = 2 we derive the posterior and marginal distributions, under both a normalgamma and Jeffrey's priors. The predictive distribution is also derived and an application to the Canadian Lynx series is given. Key words: ARMA models; Bayesian analysis; threshold;predictive distribution; TARMA models. 1. Introduction In this paper we extend in a simple way the works of Broemeling and Shaarawy(1988) and Broemeling and Cook(1992) on the Bayesi...
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".