Dynamic Bayesian smooth transition autoregressive (DBSTAR) models for non-stationary nonlinear time series
Bibliographic record
Abstract
In this thesis, Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models are proposed for nonlinear time series, as an alternative to both the classical Smooth Transition Autoregressive (STAR) models and Computational Bayesian STAR (CBSTAR) models. DBSTAR models are autoregressive formulations of dynamic linear models based on polynomial approximations of transition functions of STAR models. Unlike classical STAR and CBSTAR models, their parameters vary in time, being suitable for modelling both global and local non-stationary processes. Since DBSTAR models are Bayesian, the models do not require extensive historical data for parametric estimation and allow expert intervention via prior distribution assessment of model parameters. Because they are analytical and sequential, DBSTAR models, respectively, avoid potential computational problems associated with CBSTAR models, such as convergence issues, and allow fast estimation of dynamic parameters sequentially in time, being thus suitable for real time applications. Proposed DBSTAR models have been applied to two data sets: the much used Canadian Lynx data set, in which the aim is to validate DBSTAR models by comparing their fitting performances with existing approaches in the literature, and a Brazilian electricity load data set, for which existing models are not suitable.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".