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Enregistrement W4243520502 · doi:10.1108/s0731-905320160000036009

Acknowledgments

2016· other· en· W4243520502 sur OpenAlexaboutno aff

Notice bibliographique

Revuenon disponible
Typeother
Langueen
DomaineComputer Science
ThématiqueData Analysis with R
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPublishingHonourLibrary scienceHonorSpatial econometricsManagementSociologyPolitical scienceStatisticsEconomicsComputer scienceMathematicsLaw

Résumé

récupéré en direct d'OpenAlex

Citation (2016), "Acknowledgments", Essays in Honor of Aman Ullah (Advances in Econometrics, Vol. 36), Emerald Group Publishing Limited, Bingley, pp. xxiii-xxiv. https://doi.org/10.1108/S0731-905320160000036009 Publisher: Emerald Group Publishing Limited Copyright © 2016 Emerald Group Publishing Limited A conference in honour of Aman Ullah took place at Riverside Mission Inn on March 13–15, 2015 to celebrate his long and productive academic career. We would like to thank the following sponsors for their support (in alphabetical order): editors of Advances in Econometrics, Emerald Group Publishing Ltd., Info-Metrics Institute of American University, International Association for Applied Econometrics (IAAE) and the University of California Riverside’s College of Humanities Arts and Social Sciences, Department of Economics, Department of Statistics, School of Business Administration and School of Public Policy. We also like to express our thanks to the following UC-Riverside staff: Damaris Carlos, Gary Kuzas, Mark Manalang, Tanya Wine (and Mark Wine) and Patricia Winkler, and two of our graduate students: Bai Huang and Najrin Khanom. Their hard work and dedication before, during, and after the event made our work much easier. This AIE volume is a collection of papers, most of them presented at the conference, on topics in which Aman Ullah has made major contributions – nonparametric and semiparametric econometrics, finite sample econometrics, shrinkage methods, information/entropy econometrics, model specification testing, robust inference and panel/spatial models. Each paper was rigorously refereed by two or three experts. We would like to thank the following reviewers for their critical and constructive reviews (in alphabetical order): Deniz Baglan (Howard University), Yong Bao (Purdue University), Martin Burda (University of Toronto), Zongwu Cai (University of Kansas), Matias Cattaneo (University of Michigan), John Chao (University of Maryland), Juan Carlos Escanciano (Indiana University), Yanqin Fan (University of Washington), Jiti Gao (Monash University), Amos Golan (American University), William Greene (New York University), Daniel Henderson (University of Alabama), Eric Hillebrand (University of Aarhus), Cheng Hsiao (University of Southern California), Emma Iglesias (University of Coruña), Atsushi Inoue (Vanderbilt University), David Jacho-Chavez (Emory University), Ivan Jeliazkov (University of California, Irvine), Fei Jin (Shanghai University of Finance and Economics), Degui Li (University of York), Dong Li (University of Texas Dallas), Qi Li (Texas A&M), Wei Lin (Capital University of Economics and Business), Chu-An Liu (Academia Sinica), Essie Maasoumi (Emory University), Ron Mittelhammer (Washington State University), Kusum Mundra (Rutgers University), Kelley Pace (Louisiana State University), Christopher Parmeter (University of Miami), Hashem Pesaran (University of Southern California), Jeff Racine (McMaster University), Chris Skeels (University of Melbourne), Xiaojun Song (Peking University), Liangjun Su (Singapore Management University), Yuying Sun (Chinese Academy of Sciences), Abderrahim Taamouti (Durham University), Yundong Tu (Peking University), Aman Ullah (University of California, Riverside), Alan Wan (City University of Hong Kong), Ying Wang (Peking University), Ximing Wu (Texas A&M University), Ke-Li Xu (Texas A&M University), Jui-Chung Ray Yang (University of Southern California), Zhenlin Yang (Singapore Management University) and Yonghui Zhang (Renmin University). We would also like to thank the following participants, who made presentations and/or chaired sessions: Deniz Baglan (Howard University), Anil Bera (University of Illinois), Tom Fomby (Southern Methodist University), Yan Ge (Central University of Finance and Economics), Xiao Huang (Kennesaw State University), Wei Lin (Capital University of Economics and Business), Ruixuan Liu (Emory University), Xiangdong Long (Bank of Communications Schroder Fund Management), Shujie Ma (University of California, Riverside), Kusum Mundra (Rutgers University), Xiaojun Song (Peking University), Yundong Tu (Peking University), Yun Wang (University of International Business and Economics) and Jie Wei (Huazhong University of Science and Technology). Book Chapters Essays in Honor of Aman Ullah Advances in Econometrics Essays in Honor of Aman Ullah Copyright Page List of Contributors Introduction Acknowledgments Photos Part I: Tribute A Selective Review of Aman Ullah’s Contributions to Econometrics Part II: Panel Data Models Semiparametric Estimation of Partially Linear Varying Coefficient Panel Data Models Testing for Spatial Lag and Spatial Error Dependence in a Fixed Effects Panel Data Model Using Double Length Artificial Regressions Long-Run Effects in Large Heterogeneous Panel Data Models with Cross-Sectionally Correlated Errors Semiparametric Estimation of Partially Linear Dynamic Panel Data Models with Fixed Effects Part III: Finite Sample Econometrics Finite-Sample Bias of the Conditional Gaussian Maximum Likelihood Estimator in ARMA Models Finite Sample BIAS Corrected IV Estimation for Weak and Many Instruments Part IV: Information and Entropy On the Construction of Prior Information – An Info-Metrics Approach The Wage Premium of Naturalized Citizenship Causality and Markovianity: Information Theoretic Measures Part V: Issues in Econometric Theory A Likelihood-Free Reverse Sampler of the Posterior Distribution A Vector Autoregressive Moving Average Model for Interval-Valued Time Series Data Inference in Near-Singular Regression Part VI: Nonparametric and Semiparametric Methods Multivariate Local Polynomial Estimators: Uniform Boundary Properties and Asymptotic Linear Representation Model Averaging Over Nonparametric Estimators Smoothness: Bias and Efficiency of Nonparametric Kernel Estimators A Class of Nonparametric Density Derivative Estimators Based on Global Lipschitz Conditions Local Polynomial Derivative Estimation: Analytic or Taylor? A Simple Consistent Nonparametric Estimator of the Lorenz Curve

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,340
Score d'incertitude au seuil0,977

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0240,066

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,262
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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