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...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".