Multiple change‐point models for time series
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 The “Bayes‐type” method of deriving change‐point test statistics was introduced by Chernoff and Zacks (1964). Other authors subsequently adapted this approach and derived Bayes‐type statistics for at most one change (AMOC), and for multiple change points, under a variety of model formulations. Asymptotic distribution theory has always been limited to the AMOC statistics because of the perceived complexity of multiple change‐point statistics. In this article, it is shown that the Bayes‐type statistic derived to test for multiple change points is directly proportional to the AMOC statistic. This result immediately provides distributional results for Bayes‐type multiple change‐point statistics. In addition, it fundamentally alters the current understanding of the AMOC statistic. It follows from this result that the Bayes‐type statistic derived under AMOC conditions in fact tests for at least one change (ALOC), even though the statistic is derived under AMOC formulation. Under asymptotic consideration, the result also extends to the case of model errors following a stationary process. As an example, the classical Nile River data are revisited and analyzed for the presence of multiple change points.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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