Enhanced Backward Multiple Change-Point Detection
Bibliographic record
Abstract
Many statistical tools are built upon a specific set of assumptions on the distribution of the \ndata at hand. However, the distribution of the observations in the dataset may not remain \nconstant and may change due to some external events. For a sequence of observations, \nthe points after which the distribution function has changed are commonly referred to \nas change points. Identifying such points can also be critical in gaining insights into the \ndistributional behaviour of random variables and constructing statistical models. Thus, \nthe change points analysis potentially applies to almost all data-driven disciplines, such as \nbiology, finance, and public policies. \nChange points analysis is categorized into online and offline analysis. The online change \npoints analysis is designed to detect changes in the distribution of random variables as \nnew observations are introduced. On the other hand, offline analysis is concerned with \nrecovering change points within a historical dataset. In this thesis, we are only concerned \nwith offline change point analysis; for simplicity, we refer to offline change points analysis \nas change points analysis. \nChange point analysis was born 70 years ago from the quality control discipline Page \n(1954). Initially, the main focus of the change points literature was on the single change \npoint scenario in which, at most, one change point exists within a sequence of random vari- \nables. However, with the advent of computers, the focus has switched to multiple change \npoint detection problems. This shift does not imply that single change point detection \nmethods are irrelevant. For instance, many multiple change point detection methods re- \ncover change points by conducting a single change point test locally. This class of change \npoint detection methods is called local search methods. \nOne of the primary concerns of local search methods is the application of a single change \npoint test statistic within the largest possible segment of the sequence of random variables \nwith exactly one change point. Obtaining such intervals is a difficult task. For instance, \nwild binary segmentation Fryzlewicz et al. (2014) extracts the change points from intervals \ncontaining multiple change points. On the other hand, the narrowest over threshold Bara- \nnowski et al. (2019) estimates the change points within the narrowest intervals in which a \npredefined threshold is satisfied. Thus, the accuracy of the estimated locations of change \npoints may suffer due to the shortness of these intervals. In this thesis, we propose two local \nsearch methods that attempt to infer locations of change points within the desirable in- \ntervals. The first method, enhanced backward detection (EBD), recovers the change points \nby eliminating unlikely candidates sequentially. The second method, i.e., narrowest over \nthreshold via interval selection with shortened exhaustive search (NOT-IS.SES), estimates the location of change points by following a top-down approach. That is, the change points \nare added to the active set sequentially. EBD and NOT-IS.SES are general procedures that \ncan be applied to a wide range of change point problems by simply changing the underlying \nsingle change point test statistics.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".