Bayesian Panel Quantile Regression for Binary Outcomes with Correlated\n Random Effects: An Application on Crime Recidivism in Canada
Bibliographic record
Abstract
This article develops a Bayesian approach for estimating panel quantile\nregression with binary outcomes in the presence of correlated random effects.\nWe construct a working likelihood using an asymmetric Laplace (AL) error\ndistribution and combine it with suitable prior distributions to obtain the\ncomplete joint posterior distribution. For posterior inference, we propose two\nMarkov chain Monte Carlo (MCMC) algorithms but prefer the algorithm that\nexploits the blocking procedure to produce lower autocorrelation in the MCMC\ndraws. We also explain how to use the MCMC draws to calculate the marginal\neffects, relative risk and odds ratio. The performance of our preferred\nalgorithm is demonstrated in multiple simulation studies and shown to perform\nextremely well. Furthermore, we implement the proposed framework to study crime\nrecidivism in Quebec, a Canadian Province, using a novel data from the\nadministrative correctional files. Our results suggest that the recently\nimplemented "tough-on-crime" policy of the Canadian government has been largely\nsuccessful in reducing the probability of repeat offenses in the post-policy\nperiod. Besides, our results support existing findings on crime recidivism and\noffer new insights at various quantiles.\n
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".