On AR(1) versus MA(1) models for Non-stationary time series of Poisson counts: part I (theory)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: Analysis of time series of counts is an important research topic in many bio-medical and socio-economic sectors. For example, analyzing the yearly number of patients of a particular disease in a country is an important problem for health economics. Similarly, analyzing the monthly number of tourists for a city/country and the yearly number of patents awarded to a firm are important economic problems. Unlike in the Gaussian time series case, the analysis of this type of count data is, however, not easy due to the difficulty of modelling the correlated count data recorded over a long period of time. The problem becomes much more difficult if the counts are non-stationary over time, which is likely to be the case in many practical situations. Recently, some authors have developed Gaussian type non-stationary AR(1) (auto-regressive of order 1) models to fit the time series of count data. But, as in practice, there may be situations where Gaussian type moving average (MA) models may fit the count data better than the AR models, this paper develops a non-stationary MA(1) model and compare its basic properties with those of the AR(1) model. For the purpose of statistical inference, the parameters of the proposed models are estimated through an efficient quasi-likelihood (QL) approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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