Essays on Testing Structural Changes and Constant Conditional Dependence Via the Fourier Transform
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IMS Grace Wahba Award and Lecture
Volume 50 • Issue 4 IMS Bulletin June/July 2021 IMS Grace Wahba Award and Lecture The IMS is pleased to announce the creation of a new award and lecture, to honor CONTENTS Grace Wahba’s monumental contributions to statistics and science. These include her 1 New IMS Grace Wahba Award pioneering and influential work in mathematical statistics, machine learning and opti- and Lecture mization; broad and career-long interdisciplinary collaborations that have had a sig- 2 Members’ news : Kenneth nificant impact in epidemiology, bioinformatics and climate sciences; and outstanding Lange, Kerrie Mengersen, Nan mentoring during her 51-year career as an educator. The inaugural Wahba award and Laird, Daniel Remenik, Richard lecture is planned for the 2022 IMS annual meeting in London, then annually at JSM. Samworth Grace Wahba is one of the outstanding statisticians of our age. She has transformed 4 Grace Wahba: profile the fields of mathematical statistics and applied statistics. Wahba’s RKHS theory plays a central role in nonparametric smoothing and splines, and its importance is widely 7 Caucus for Women in recognized. In addition, Wahba’s contributions Statistics 50th anniversary straddle the boundary between statistics and 8 IMS Travel Award winners optimization, and have led to fundamental break- 9 Radu’s Rides: Notes to my Past throughs in machine learning for solving problems Self arising in prediction, classification and cross-vali- dation. She has paved a foundation for connecting 10 Previews: Nancy Zhang, Ilmun Kim theory and practice of function estimation, and has developed, along with her students, unified 11 Nominate IMS Lecturers estimation methods, scalable algorithms and 12 IMS Fellows 2021 standard software toolboxes that have made regu- 16 Recent papers: AIHP and larization approaches widely applicable to solving Observational Studies complex problems in modern science discovery Grace Wahba and technology innovation. -
Coauthorship and Citation Networks for Statisticians
Submitted to the Annals of Applied Statistics arXiv: arXiv:0000.0000 COAUTHORSHIP AND CITATION NETWORKS FOR STATISTICIANS By Pengsheng Jiy and Jiashun Jinz University of Georgiay and Carnegie Mellon Universityz We have collected and cleaned two network data sets: Coauthor- ship and Citation networks for statisticians. The data sets are based on all research papers published in four of the top journals in statis- tics from 2003 to the first half of 2012. We analyze the data sets from many different perspectives, focusing on (a) centrality, (b) commu- nity structures, and (c) productivity, patterns and trends. For (a), we have identified the most prolific/collaborative/highly cited authors. We have also identified a handful of \hot" papers, suggesting \Variable Selection" as one of the \hot" areas. For (b), we have identified about 15 meaningful communities or research groups, including large-size ones such as \Spatial Statis- tics", \Large-Scale Multiple Testing", \Variable Selection" as well as small-size ones such as \Dimensional Reduction", \Objective Bayes", \Quantile Regression", and \Theoretical Machine Learning". For (c), we find that over the 10-year period, both the average number of papers per author and the fraction of self citations have been decreasing, but the proportion of distant citations has been increasing. These suggest that the statistics community has become increasingly more collaborative, competitive, and globalized. Our findings shed light on research habits, trends, and topological patterns of statisticians. The data sets provide a fertile ground for future researches on or related to social networks of statisticians. 1. Introduction. It is frequently of interest to identify \hot" areas and key authors in a scientific community, and to understand the research habits, trends, and topological patterns of the researchers. -
Strength in Numbers: the Rising of Academic Statistics Departments In
Agresti · Meng Agresti Eds. Alan Agresti · Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics DepartmentsStatistics in the U.S. Rising of Academic The in Numbers: Strength Statistics Departments in the U.S. Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. Alan Agresti • Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. 123 Editors Alan Agresti Xiao-Li Meng Department of Statistics Department of Statistics University of Florida Harvard University Gainesville, FL Cambridge, MA USA USA ISBN 978-1-4614-3648-5 ISBN 978-1-4614-3649-2 (eBook) DOI 10.1007/978-1-4614-3649-2 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012942702 Ó Springer Science+Business Media New York 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. -
(G)ARCH Processes and the Phenomenon of Misleading and Unambiguous Signals
(G)ARCH processes and the phenomenon of misleading and unambiguous signals Ana Beatriz Torres de Sousa Thesis to obtain the Master of Science Degree in Mathematics and Applications Supervisor: Prof. Manuel Jo˜ao Cabral Morais Examination Committee Chairperson: Prof. Ant´onio Manuel Pacheco Pires Supervisor: Prof. Manuel Jo˜ao Cabral Morais Members of the Committee: Prof. Patr´ıcia Alexandra de Azevedo Carvalho Ferreira e Pereira Ramos December 2015 Acknowledgments During the preparation of this dissertation, I received the help and support of several people to whom I would like to thank. First of all, I want to express my genuine gratitude to my supervisor, Professor Manuel Cabral Morais, for his guidance, constructive criticism, proofreading and specially for his patience during our weekly and stimulating meetings via Skype. My thanks are also extensive to: Professor Yarema Okhrin because without his programming sug- gestion I would have waited much longer for the simulations to end; and Professor Wolfgang Schmid for providing crucial critical values and, thus, saving me from excruciating computations and time consuming simulations. This thesis could not have been written without the support and infinite patience of my family and friends. I am deeply grateful to: my parents, Manuela and Fernando, for always being there pushing me forward; my sister Rita who showed me that even the hardest obstacles can be overcome with persistence; and my grandmother Alice who always believed in me even when I did not. i ii Resumo Uma s´erie temporal ´euma sequˆencia de observa¸c˜oes ordenadas no tempo. No estudo destas sequˆencias, ´enecess´ario reconhecer explicitamente a importˆancia da ordem pela qual as observa¸c˜oes s˜ao recolhidas, logo recorrer `aan´alise e aos modelos de s´eries temporais. -
CFE-Cmstatistics 2020 Book of Abstracts
CFE-CMStatistics 2020 PROGRAMME AND ABSTRACTS 14th International Conference on Computational and Financial Econometrics (Virtual CFE 2020) http://www.cfenetwork.org/CFE2020 and 13th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (Virtual CMStatistics 2020) http://www.cmstatistics.org/CMStatistics2020 19 – 21 December 2020 Computational and Methodological Statistics CMStatistics Computational and CFENetwork Financial Econometrics ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. I CFE-CMStatistics 2020 ISBN 978-9963-2227-9-7 ⃝c 2020 - ECOSTA ECONOMETRICS AND STATISTICS All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any other form or by any means without the prior permission from the publisher. II ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. CFE-CMStatistics 2020 International Organizing Committee: Ana Colubi, Erricos Kontoghiorghes and Manfred Deistler. CFE 2020 Co-chairs: Anurag Banerjee, Scott Brave, Peter Pedroni and Mike So. CFE 2020 Programme Committee: Knut Are Aastveit, Alessandra Amendola, David Ardia, Josu Arteche, Anindya Banerjee, Travis Berge, Mon- ica Billio, Raffaella Calabrese, Massimiliano Caporin, Julien Chevallier, Serge Darolles, Luca De Angelis, Filippo Ferroni, Ana-Maria Fuertes, Massimo Guidolin, Harry Haupt, Masayuki Hirukawa, Benjamin Hol- cblat, Rustam Ibragimov, Laura Jackson Young, Michel Juillard, Edward Knotek, Robinson Kruse-Becher, Svetlana Makarova, Ilia Negri, Ingmar Nolte, Jose Olmo, Yasuhiro Omori, Jesus Otero, Michael Owyang, Alessia Paccagnini, Indeewara Perera, Jean-Yves Pitarakis, Tommaso Proietti, Artem Prokhorov, Tatevik Sekhposyan, Etsuro Shioji, Michael Smith, Robert Taylor, Martin Wagner and Ralf Wilke. CMStatistics 2020 Co-chairs: Tapabrata Maiti, Sofia Olhede, Michael Pitt, Cheng Yong Tang and Tim Verdonck. -
Robust High Dimensional Factor Models with Applications to Statistical Machine Learning∗
Robust high dimensional factor models with applications to statistical machine learning∗ Jianqing Fanyz, Kaizheng Wangy, Yiqiao Zhongy, Ziwei Zhuy August 14, 2018 Abstract Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSe- lect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and pro- vide powerful tools for statistical estimation and inference. We highlight PCA and its connec- tions to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery. Key Words: Factor model, PCA, covariance estimation, perturbation bounds, robustness, random sketch, FarmSelect, FarmTest 1 Introduction In modern data analytics, dependence across high-dimensional outcomes or measurements is ubiq- uitous. For example, stocks within the same industry exhibit significantly correlated returns, hous- ing prices of a country depend on various economic factors, gene expressions can be stimulated arXiv:1808.03889v1 [stat.ME] 12 Aug 2018 by cytokines. -
Computational and Financial Econometrics (CFE 2017)
CFE-CMStatistics 2017 PROGRAMME AND ABSTRACTS 11th International Conference on Computational and Financial Econometrics (CFE 2017) http://www.cfenetwork.org/CFE2017 and 10th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2017) http://www.cmstatistics.org/CMStatistics2017 Senate House & Birkbeck University of London, UK 16 – 18 December 2017 ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. I CFE-CMStatistics 2017 ISBN 978-9963-2227-4-2 ⃝c 2017 - ECOSTA ECONOMETRICS AND STATISTICS Technical Editors: Gil Gonzalez-Rodriguez and Marc Hofmann. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any other form or by any means without the prior permission from the publisher. II ⃝c ECOSTA ECONOMETRICS AND STATISTICS. All rights reserved. CFE-CMStatistics 2017 International Organizing Committee: Ana Colubi, Erricos Kontoghiorghes, Marc Levene, Bernard Rachet, Herman Van Dijk. CFE 2017 Co-chairs: Veronika Czellar, Hashem Pesaran, Mike Pitt and Stefan Sperlich. CFE 2017 Programme Committee: Knut Are Aastveit, Alessandra Amendola, Josu Arteche, Monica Billio, Roberto Casarin, Gianluca Cubadda, Manfred Deistler, Jean-Marie Dufour, Ekkehard Ernst, Jean-David Fermanian, Catherine Forbes, Philip Hans Franses, Marc Hallin, Alain Hecq, David Hendry, Benjamin Holcblat, Jan Jacobs, Degui Li, Alessandra Luati, Richard Luger, J Isaac Miller, Claudio Morana, Bent -
Biographical Sketch for Jianqing Fan
Biographical Sketch for Jianqing Fan Jianqing Fan is Frederick L. Moore'18 Professor of Finance and Director of Commit- tee of Statistical Studies at Princeton University, and the past president of the Institute of Mathematical Statistics (2006-2009), and president of International Chinese Statistical Association. He is the Co-editor of Econometrical Journal published by Royal Economics Society and an associate editor of The Journal of American Statistical Association, and was the co-editor(-in-chief) of The Annals of Statistics (2004-2006) and an editor of Prob- ability Theory and Related Fields (2003-2005) and on the editorial boards of a number of other journals. After receiving his Ph.D. in Statistics from the University of California at Berkeley, he has been appointed as assistant, associate, and full professor at the Univer- sity of North Carolina at Chapel Hill (1989-2003), and as professor at the University of California at Los Angeles (1997-2000), Professor of Statistics and Chairman at the Chinese University of Hong Kong (2000-2003), and as professor at the Princeton University(2003{). He has coauthored two highly-regarded books on \Local Polynomial Modeling" (1996) and \Nonlinear time series: Parametric and Nonparametric Methods" (2003) and authored or coauthored over 150 articles on computational biology, ¯nancial econometrics, semipara- metric and non-parametric modeling, statistical learning, nonlinear time series, survival analysis, longitudinal data analysis, and other aspects of theoretical and methodological statistics. -
Volumen 53 Volumen 64
ESTESTADISTICAADÍSTICA volumen 53 volumen 64 Junio y Diciembre 2012 número 182 y 183 REVISTREVISTAA DEL SEMESTRAL INSTITUTO DEL INSTITUTO INTERAMERICANOINTERAMERICANO DE DE EST ESTADÍSTICAADÍSTICA BIANNUAL JOURNALJOURNAL OF THTHEE INTER-AMERICANINTER-AMERICAN ST ASTATISTICALTISTICAL INSTITUTE INSTITUTE EDITORA EN JEFE / EDITOR IN CHIEF CLYDE CHARRE DE TRABUCHI Consultora/Consultant French 2740, 5º A 1425 Buenos Aires, Argentina Tel (54-11) 4824-2315 e-mail [email protected] e-mail [email protected] EDITORA EJECUTIVA / EXECUTIVE EDITOR VERONICA BERITICH Instituto Nacional de Estadística y Censos (INDEC) Ramallo 2846 1429 Buenos Aires, Argentina Tel (54-11) 4703-0585 e-mail [email protected] EDITORES ASOCIADOS / ASSOCIATE EDITORS D. ANDRADE G. ESLAVA P. MORETTIN Univ. Fed. Sta. Catalina, BRASIL UNAM, MEXICO Univ. de Sao Pablo, BRASIL M. BLACONA A. GONZALEZ VILLALOBOS F. NIETO Univ. de Rosario, ARGENTINA Consultor/Consultant, ARGENTINA Univ. de Colombia, COLOMBIA J. CERVERA FERRI V. GUERRERO GUZMAN J. RYTEN Consultor/Consultant, ESPAÑA ITAM, MEXICO Consultor/Consultant, CANADA E. DAGUM R. MARONNA * S. SPECOGNA Consultor/Consultant, CANADA Univ. de La Plata, ARGENTINA ANSES, ARGENTINA E. de ALBA I. MENDEZ RAMIREZ P. VERDE INEGI, MEXICO IIMAS/UNAM, MEXICO University of Düsseldorf, ALEMANIA P. do NASCIMENTO SILVA M. MENDOZA RAMIREZ V. YOHAI ENCE/IBGE, BRASIL ITAM, MEXICO Univ. de Buenos Aires, ARGENTINA L. ESCOBAR R. MENTZ Louisiana State Univ., USA Univ. de Tucumán, ARGENTINA * Asistente de las Editoras / Editors’ Assistant ESTADÍSTICA (2012), 64, 182 y 183, pp. 5-22 © Instituto Interamericano de Estadística JAMES DURBIN: IN MEMORIAM JUAN CARLOS ABRIL Universidad Nacional de Tucumán, Facultad de Ciencias Económicas y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). -
Maximum Likelihood
PSCI 7155: Maximum Likelihood University of Colorado Boulder Fall 2017 Time: Wednesday, 3:30-6:00 Location: HLMS 196 Instructor: Dr. Andrew Q. Philips Office: KTCH 144 Email: [email protected] Office hours: Thursday 12:00-1:45 or by appointment COURSE DESCRIPTION: Models utilizing maximum likelihood (ML) estimation are ubiquitous in political science. This course is designed to introduce you to a variety of ML estimators. We will start with a simple overview of probability models and show why we might need such an estimation tech- nique. After discussing likelihood theory, we then move to more “novel” types of data, such aslinear models with censoring and truncation, binary choice models, count models, and duration models. Much of the class will be spent on learning how to interpret these models. The last two weeks are spe- cial topics that we will decide on during the first weeks of class, and may cover topics such as bounded and compositional data, interpreting linear and non-linear interactions, and imputing missing data. These last topics will be chosen based on time constraints and class interests. By the end of this course you should be able to: • Understand what is going on “under the hood” when estimating maximum likelihood models. • Identifying which parametric model best fits your research question and the underlying data- generating process. • Be able to interpret a variety of ML models and present your results using predictions, substan- tive interpretations, simulations, etc. • Apply what you have learned to your research. PREREQUISITES: This an graduate level course; students should have a background in at least introductory (and preferably) advanced regression statistics (i.e., Data I and Data II). -
Embracing the Blessing of Dimensionality in Factor Models
Embracing the Blessing of Dimensionality in Factor Models Quefeng Li, Guang Cheng, Jianqing Fan and Yuyan Wang ∗ October 18, 2016 Abstract Factor modeling is an essential tool for exploring intrinsic dependence structures among high-dimensional random variables. Much progress has been made for esti- mating the covariance matrix from a high-dimensional factor model. However, the blessing of dimensionality has not yet been fully embraced in the literature: much of the available data is often ignored in constructing covariance matrix estimates. If our goal is to accurately estimate a covariance matrix of a set of targeted variables, shall we employ additional data, which are beyond the variables of interest, in the estimation? In this paper, we provide sufficient conditions for an affirmative answer, and further quantify its gain in terms of Fisher information and convergence rate. In fact, even an oracle-like result (as if all the factors were known) can be achieved when a sufficiently large number of variables is used. The idea of utilizing data as much as possible brings computational challenges. A divide-and-conquer algorithm is thus proposed to alleviate the computational burden, and also shown not to sacrifice any statistical accuracy in comparison with a pooled analysis. Simulation studies further confirm our advocacy for the use of full data, and demonstrate the effectiveness of the above algorithm. Our proposal is applied to a microarray data example that shows empirical benefits of using more data. Keywords: Asymptotic normality, auxiliary data, divide-and-conquer, factor model, Fisher information, high-dimensionality. ∗Quefeng Li is Assistant Professor, Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599 (Email:[email protected]). -
JAMES DURBIN: UNA VIDA DEDICADA a LA Estadística Juan Carlos Abril James Durbin: Una Vida Dedicada a La Estadística
PERFIL BIOGRÁFICO: JAMES DURBIN: UNA VIDA DEDICADA A LA ESTADÍSTICA JUAN CARLOS ABRIL James Durbin: una vida dedicada a la estadística Juan Carlos Abril Master of Science in Statistics (London School of Economics) [email protected] Doctor of Philosophy (London School of Economics) Universidad Nacional de Tucumán y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina Resumen: El Profesor James Durbin ha fallecido en la tarde del sábado 23 de Junio de 2012, en Londres, a la edad de 88 años. Fue una de las figuras más importantes de la Estadística. Sus contribuciones cubren áreas de muestreo, teoría de las distribuciones, estadística no paramétrica, procesos estocásticos, y por sobre todo series de tiempo y econometría. Por su trascendencia científica, sus aportes a la Estadística y por la gran amistad de más de 38 años que me unía a él, presento este homenaje en donde se resaltan la trayectoria y la personalidad del Profesor Durbin, y sus importantes contribuciones a la ciencia. Palabras clave: Durbin, Econometría, Estadística, Series de Tiempo. ESTATÍSTICA E SOCIEDADE, Porto Alegre, p.122-149, n.3 dez. 2013 | seer.ufrgs.br/estatisticaesociedade 122 PERFIL BIOGRÁFICO: JAMES DURBIN: UNA VIDA DEDICADA A LA ESTADÍSTICA JUAN CARLOS ABRIL James Durbin: a life devoted to statistics Abstract: Professor James Durbin has died in the afternoon of Saturday June 23, 2012, in London, at the age of 88. He was one of the most important figures of Statistics. His contributions cover areas of sampling, theory of distributions, nonparametric statistics, stochastic processes, and above all time series and econometrics.