Bibliographic record
Abstract
This work proposes a general structure for inference in modelling discrete data sets using a count time series model with INGARCH models with a log-linear structure and mixed Poisson innovations. To this end, a class of probability distributions will be used, the main objective of which is to model count data over time that present a condition of overdispersion. More specifically, the work presents two particular cases: the Inverse Gaussian Poisson log-linear distribution and the Negative Binomial log-linear distribution, which are obtained by considering cases of unobservable data that follow the Inverse Gaussian and Gamma distributions, respectively. The distributions inserted through the mean have as a common point the fact that they are members of the exponential family of distributions. The iterative maximum likelihood method will be used to estimate the model parameters using the EM algorithm. The performance of the estimators will be evaluated through simulation studies using the Monte Carlo method, considering different sample sizes to evaluate the asymptotic behaviour of these estimators.In the section on applying the proposed model to real data sets, three databases were considered for analysis: the first lists the number of hospitalisations due to alcohol abuse in the state of Paraíba, the second evaluates the same problem, but with the data presented for the state of Piauí and, finally, the database consisting of the number of cases of Campylobacter infections in the province of Quebec in Canada was evaluated, thus closing the section on applications to real data. The simulation data was tested using the two proposed extensions and the comparison model called log-linear Poisson proposed by [9], initially taking into account a graphical analysis of the behaviour of the sample, autocorrelation and partial autocorrelation, the study of simulation by means of convergence taking into account the values obtained and the observation of graphs representing a generalised view of the layout of the simulation data. Subsequently, a reflection was made on its effectiveness through information criteria and the mean square error used in the process of evaluating and choosing the best regression model to adjust the data.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 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".