Special issue in honour of Nancy Reid: Guest Editors' introduction
Notice bibliographique
Résumé
We are delighted to present a special issue of The Canadian Journal of Statistics (CJS) in honour of Professor Nancy Reid.The articles in this collection have been contributed by a group of participants who attended a workshop entitled "Statistics at its Best" in Toronto on 5 May 2022.The workshop was organized by the Department of Statistical Sciences at the University of Toronto to celebrate Professor Reid's 70th birthday.It highlighted her remarkable contributions to Statistical Science and her dedication to the profession, exemplified in research, leadership, service and education of the next generation of statisticians.Professor Reid's impactful career has played a crucial role in fostering the growth of the Canadian statistical community.This workshop was part of a series of celebratory activities coordinated by the Statistical Society of Canada, marking the 50th anniversary of the statistical community in this country.This collection of articles encompasses a wide range of topics.First, the engaging dialogue A conversation with Nancy Reid by Craiu and Yi sheds light on Professor Reid's intellectual journey and perspectives on statistical science and data science.In The inducement of population sparsity, Battey presents the pioneering work on parameter orthogonalization by Cox and Reid as an inducement of abstract population-level sparsity.The article focuses on three important examples related to sparsity-inducing parameterizations or data transformations: covariance models, nuisance parameter elimination and high-dimensional regression.Strategies for inducing sparsity vary depending on the context and may involve solving partial differential equations or specifying parameterized paths.Battey concludes by presenting some open problems.McCullagh then highlights, in A tale of two variances, the ambiguity and potential misinterpretation of the standard repeated-sampling concept of the variance in a finite-dimensional parametric model.He presents three operational interpretations, all numerically distinct and compatible with repeated sampling from a fixed parameter population.These interpretations help resolve contradictions between Fisherian variance and inverse-information variance.We next turn to hypothesis testing for parameters on the boundary of their domain.In Improved inference for a boundary parameter, Elkantassi, Bellio, Brazzale and Davison review theoretical work on the problem, including hard and soft boundaries, and iceberg estimators.They highlight the significant underestimation of the probability due to the limiting results, propose remedies based on the normal approximation for the profile score function, and outline the success of higher order approximations.Using these approaches, the authors develop an accurate test to assess the need for a spline component in a linear mixed model.In Sparse estimation within Pearson's system, with an application to financial market risk, Carey, Genest and Ramsay tackle the challenging task of estimating a density within Pearson's system, a class of models encompassing many classical univariate distributions.The authors propose an effective method by combining penalized regression and profiled estimation techniques.Through simulations and an application using S&P 500 data, they demonstrate that the method improves market risk assessment substantially, outperforming the value-at-risk and expected shortfall estimates currently used by financial institutions and regulators.Urban, Bong, Orellana and Kass explore Oscillating neural circuits: Phase, amplitude, and the complex normal distribution.They consider multiple oscillating time series in the frequency domain and discuss the complex-valued correlation, its similarities to real-valued Pearson
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,009 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,010 | 0,004 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,013 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 0,038 |
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 ».