Enhanced Backward Multiple Change-Point Detection
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».