Simple and Multiple Change Point Detection in Multiple Linear Regression and Application to Hydroclimatic Variables
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Bibliographic record
Abstract
Two Bayesian methods of changepoint detection in multivariate linear regression are proposed. The first approach allows simultaneous single changepoint detection in a multivariate sample. It improves on recently published changepoint detection methodologies by allowing a more flexible prior specification for the existence of a change, the date of change and for the regression parameters. The estimation of parameters is achieved by MCMC simulations. The second approach is a multiple changepoint detection model in multivariate linear regression. A new class of priors for the parameters of the multivariate linear model is introduced and useful formulas are derived that permit straightforward computation of the posterior distribution of the changepoints. The second method is numerically efficient and does not involve MCMC simulation. It allows fast simulation of the probability of each possible number of changepoints and the posterior probability distribution of each changepoint conditional on the number of changes.
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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) | 0.000 | 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