Recursion‐based multiple changepoint detection in multiple linear regression and application to river streamflows
Bibliographic record
Abstract
A large number of models in hydrology and climate sciences rely on multiple linear regression to explain the link between key variables. The relationship in the physical world may experiment sudden changes because of climatic, environmental, or anthropogenic perturbations. To deal with this issue, a Bayesian method of multiple changepoint detection in multiple linear regression is proposed in this paper. It is an adaptation of the recursion‐based multiple changepoint method of Fearnhead (2005, 2006) to the classical multiple linear model. A new class of priors for the parameters of the multiple linear model is introduced, and useful formulas are derived that permit straightforward computation of the posterior distribution of the changepoints. The proposed method is numerically efficient and does not involve time consuming Monte‐Carlo Markov Chain simulation as opposed to other Bayesian changepoint methods. It allows fast and straightforward simulation of the probability of each possible number of changepoints as well as the posterior probability distribution of each changepoint conditional on the number of changes. The approach is validated on simulated data sets and then compared to the methodology of Seidou et al. (2006) on two practical problems, as follows: (1) the changepoint detection in the multiple linear relationship between mean basin scale precipitation at different periods of the year and the summer‐autumn flood peaks of the Broadback River located in Northern Quebec, Canada; and (b) the detection of trend variations in the streamflows of the Ogoki River located in the province of Ontario, Canada.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".