A Conversation with Tze Leung Lai Ying Lu Stanford University And
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Century of Mathematics in America, Peter Duren Et Ai., (Eds.), Vol
Garrett Birkhoff has had a lifelong connection with Harvard mathematics. He was an infant when his father, the famous mathematician G. D. Birkhoff, joined the Harvard faculty. He has had a long academic career at Harvard: A.B. in 1932, Society of Fellows in 1933-1936, and a faculty appointmentfrom 1936 until his retirement in 1981. His research has ranged widely through alge bra, lattice theory, hydrodynamics, differential equations, scientific computing, and history of mathematics. Among his many publications are books on lattice theory and hydrodynamics, and the pioneering textbook A Survey of Modern Algebra, written jointly with S. Mac Lane. He has served as president ofSIAM and is a member of the National Academy of Sciences. Mathematics at Harvard, 1836-1944 GARRETT BIRKHOFF O. OUTLINE As my contribution to the history of mathematics in America, I decided to write a connected account of mathematical activity at Harvard from 1836 (Harvard's bicentennial) to the present day. During that time, many mathe maticians at Harvard have tried to respond constructively to the challenges and opportunities confronting them in a rapidly changing world. This essay reviews what might be called the indigenous period, lasting through World War II, during which most members of the Harvard mathe matical faculty had also studied there. Indeed, as will be explained in §§ 1-3 below, mathematical activity at Harvard was dominated by Benjamin Peirce and his students in the first half of this period. Then, from 1890 until around 1920, while our country was becoming a great power economically, basic mathematical research of high quality, mostly in traditional areas of analysis and theoretical celestial mechanics, was carried on by several faculty members. -
An Annotated Bibliography on the Foundations of Statistical Inference
• AN ANNOTATED BIBLIOGRAPHY ON THE FOUNDATIONS OF STATISTICAL INFERENCE • by Stephen L. George A cooperative effort between the Department of Experimental • Statistics and the Southeastern Forest Experiment Station Forest Service U.S.D.A. Institute of Statistics Mimeograph Series No. 572 March 1968 The Foundations of Statistical Inference--A Bibliography During the past two hundred years there have been many differences of opinion on the validity of certain statistical methods and no evidence that ,. there will be any general agreement in the near future. Also, despite attempts at classification of particular approaches, there appears to be a spectrum of ideas rather than the existence of any clear-cut "schools of thought. " The following bibliography is concerned with the continuing discussion in the statistical literature on what may be loosely termed ''the foundations of statistical inference." A major emphasis is placed on the more recent works in this area and in particular on recent developments in Bayesian analysis. Invariably, a discussion on the foundations of statistical inference leads one to the more general area of scientific inference and eventually to the much more general question of inductive inference. Since this bibliography is intended mainly for those statisticians interested in the philosophical foundations of their chosen field, and not for practicing philosophers, the more general discussion of inductive inference was deliberately de-emphasized with the exception of several distinctive works of particular relevance to the statistical problem. Throughout, the temptation to gather papers in the sense of a collector was resisted and most of the papers listed are of immediate relevance to the problem at hand. -
Computation of Expected Shortfall by Fast Detection of Worst Scenarios
Computation of Expected Shortfall by fast detection of worst scenarios Bruno Bouchard,∗ Adil Reghai,y Benjamin Virrionz;x May 27, 2020 Abstract We consider a multi-step algorithm for the computation of the historical expected shortfall such as defined by the Basel Minimum Capital Requirements for Market Risk. At each step of the algorithm, we use Monte Carlo simulations to reduce the number of historical scenarios that potentially belong to the set of worst scenarios. The number of simulations increases as the number of candidate scenarios is reduced and the distance between them diminishes. For the most naive scheme, we show that p the L -error of the estimator of the Expected Shortfall is bounded by a linear combination of the probabilities of inversion of favorable and unfavorable scenarios at each step, and of the last step Monte Carlo error associated to each scenario. By using concentration inequalities, we then show that, for sub-gamma pricing errors, the probabilities of inversion converge at an exponential rate in the number of simulated paths. We then propose an adaptative version in which the algorithm improves step by step its knowledge on the unknown parameters of interest: mean and variance of the Monte Carlo estimators of the different scenarios. Both schemes can be optimized by using dynamic programming algorithms that can be solved off-line. To our knowledge, these are the first non-asymptotic bounds for such estimators. Our hypotheses are weak enough to allow for the use of estimators for the different scenarios and steps based on the same random variables, which, in practice, reduces considerably the computational effort. -
Front Matter
Cambridge University Press 978-1-107-61967-8 - Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction Bradley Efron Frontmatter More information Large-Scale Inference We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once involves more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing, and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples. bradley efron is Max H. Stein Professor of Statistics and Biostatistics at the Stanford University School of Humanities and Sciences, and the Department of Health Research and Policy at the School of Medicine. © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-1-107-61967-8 - Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction Bradley Efron Frontmatter More information INSTITUTE OF MATHEMATICAL STATISTICS MONOGRAPHS Editorial Board D. R. Cox (University of Oxford) B. Hambly (University of Oxford) S. -
IWSM2011 Program.Pdf
T h i r d International W o r k s h o p i n Sequential Methodologies Stanford University, J u n e 1 4 — 1 6 , 2 0 1 1 http://iwsm2011.stanford.edu/ The International Workshop in Sequential The first Workshop was IWSM 2007 held at Au- Methodologies is organized every two years. burn University in Alabama, USA. IWSM 2009 The Workshop covers all aspects of sequential took place at the University of Technology in methodologies in mathematical statistics and Troyes, France. information theory, from theoretical develop- ments in optimal stopping, sequential analysis, The third and present Workshop, IWSM 2011, and change detection to different applications has three main themes: in mathematical finance, quality control, clini- (i) sequential and adaptive design of clinical cal trials, and signal and image processing. The trials and other sequential methods in biosta- goal of the Workshop is to bring together re- tistics; searchers and practitioners from all areas within sequential methodologies. (ii) sequential change-point detection, quality control and surveillance, sequential methods in An additional attraction of IWSM 2011 is a sat- signal processing and sensor networks; ellite conference at Stanford immediately after- (iii) sequential estimation, testing, ranking and ward, followed by the IMS-WNAR Western Re- selection, optimal stopping and stochastic con- gional Meeting which will take place in the sce- trol, applications to finance and economics, nic city of San Luis Obispo on the central coast and related topics in statistics and probability. of California, midway between San Francisco and Los Angeles. Also, IWSM 2011 will cele- brate the 70th birthdays of both Gary Lorden of Caltech and David Siegmund of Stanford. -
Herbert S. Wilf (1931–2012)
Herbert S. Wilf (1931–2012) Fan Chung, Curtis Greene, Joan Hutchinson, Coordinating Editors received both the Steele Prize for Seminal Contri- butions to Research (from the AMS, 1998) and the Deborah and Franklin Tepper Haimo Award for Dis- tinguished Teaching (from the MAA, 1996). During his long tenure at Penn he advised twenty-six PhD students and won additional awards, including the Christian and Mary Lindback Award for excellence in undergraduate teaching. Other professional honors and awards included a Guggenheim Fellow- ship in 1973–74 and the Euler Medal, awarded in 2002 by the Institute for Combinatorics and its Applications. Herbert Wilf’s mathematical career can be divided into three main phases. First was numerical analysis, in which he did his PhD dissertation Photo courtesy of Ruth Wilf. (under Herbert Robbins at Columbia University Herb Wilf, Thanksgiving, 2009. in 1958) and wrote his first papers. Next was complex analysis and the theory of inequalities, in particular, Hilbert’s inequalities restricted to n Herbert S. Wilf, Thomas A. Scott Emeritus Professor variables. He wrote a cluster of papers on this topic, of Mathematics at the University of Pennsylvania, some with de Bruijn [1] and some with Harold died on January 7, 2012, in Wynnewood, PA, of Widom [2]. Wilf’s principal research focus during amyotrophic lateral sclerosis (ALS). He taught at the latter part of his career was combinatorics. Penn for forty-six years, retiring in 2008. He was In 1965 Gian-Carlo Rota came to the University widely recognized both for innovative research of Pennsylvania to give a colloquium talk on his and exemplary teaching: in addition to receiving then-recent work on Möbius functions and their other awards, he is the only mathematician to have role in combinatorics. -
STATISTICAL SCIENCE Volume 32, Number 3 August 2017
STATISTICAL SCIENCE Volume 32, Number 3 August 2017 AParadoxfromRandomization-BasedCausalInference............................Peng Ding 331 UnderstandingDing’sApparentParadox.......Peter M. Aronow and Molly R. Offer-Westort 346 Randomization-BasedTestsfor“NoTreatmentEffects”.......................EunYi Chung 349 InferencefromRandomized(Factorial)Experiments............................R. A. Bailey 352 AnApparentParadoxExplained..................Wen Wei Loh, Thomas S. Richardson and James M. Robins 356 Rejoinder:AParadoxfromRandomization-BasedCausalInference...............Peng Ding 362 LogisticRegression:FromArttoScience.................Dimitris Bertsimas and Angela King 367 PrinciplesofExperimentalDesignforBigDataAnalysis............Christopher C. Drovandi, Christopher C. Holmes, James M. McGree, Kerrie Mengersen, Sylvia Richardson and Elizabeth G. Ryan 385 ImportanceSampling:IntrinsicDimensionandComputationalCost..............S. Agapiou, O. Papaspiliopoulos, D. Sanz-Alonso and A. M. Stuart 405 Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas ...........................................................Michael J. Lopez and Roee Gutman 432 On the Choice of Difference Sequence in a Unified Framework for Variance Estimation in NonparametricRegression.........................Wenlin Dai, Tiejun Tong and Lixing Zhu 455 Models for the Assessment of Treatment Improvement: The Ideal and the Feasible ...................P. C. Álvarez-Esteban, E. del Barrio, J. A. Cuesta-Albertos and C. Matrán 469 Statistical Science [ISSN -
History of the Department of Statistics at Columbia University
Columbia University Statistics Tian Zheng and Zhiliang Ying Statistical Activities at Columbia Before 1946 Statistical activities at Columbia predate the formation of the department. Faculty members from other disciplines had carried out research and instruction of sta- tistics, especially in the Faculty of Political Sciences (Anderson 1955). Harold Hotelling’s arrival in 1931, a major turning point, propelled Columbia to a position of world leadership in Statistics. Hotelling’s primary research activities were in Mathematical, Economics, and Statistics but his interests had increasingly shifted toward Statistics. According to Paul Samuelson (Samuelson 1960), ‘‘It was at Columbia, in the decade before World War II, that Hotelling became the Mecca towards whom the best young students of economics and mathematical statistics turned. Hotelling’s increasing preoccupation with mathematical statistics was that discipline’s gain but a loss to the literature of economics.’’ His many contributions include canonical correlation, principal components and Hotelling’s T2. In 1938, after attending a conference of the Cowles Commission for Research in Economics, Abraham Wald, concerned by the situation in Europe, decided to stay in the United States. With Hotelling’s assistance, Wald came to Columbia on A substantial part of this chapter was developed based on Professor T. W. Anderson’s speech during our department’s sixtieth year anniversary reunion. We thank Professor Anderson for his help (as a founding member of our department) during the preparation of this chapter, our current department faculty members, Professors Ingram Olkin, and and Tze Leung Lai for their comments and suggestions on earlier versions of this chapter. Columbia University, Room 1005 SSW, MC 4690 1255 Amsterdam Avenue, New York, NY 10027, USA T. -
Book Review: What Is Mathematics? an Elementary Approach to Ideas and Methods, Volume 48, Number 11
rev-blank.qxp 10/30/01 3:00 PM Page 1325 Book Review What Is Mathematics? An Elementary Approach to Ideas and Methods Reviewed by Brian E. Blank What Is Mathematics? An Elementary Approach The book I pulled to Ideas and Methods off the shelf that day Richard Courant and Herbert Robbins did more than open Second edition, revised by Ian Stewart my eyes; it changed Oxford University Press, 1996 my life. I graduated ISBN 0-19-510519-2 from high school later that year, $19.95, 566 pages armed with a twenty- Thirty-two years ago while browsing in my high dollar book certifi- school library, I happened upon an intriguingly ti- cate. It might have tled expository book. I knew astonishingly little of been prudent to save the practice of mathematics at the time. I was my award for the aware that geometry derived from Thales, Pythago- college textbooks ras, and Euclid. Whether or not any geometric the- that I would soon re- orems were discovered after Euclid I could not quire. However, with no further access to say. That algebra and trigonometry were the results my school library, I of conscious development would no more have oc- had a more pressing need. For less than nine dol- curred to me than the idea that the English lan- lars What Is Mathematics? by Richard Courant and guage was the creation of dedicated professional Herbert Robbins became the first volume in my col- linguists. I knew the names and work of many sci- lection of mathematics books. entists—Copernicus, Kepler, and Galileo in as- tronomy; Darwin, Mendel, and Pasteur in biology; Only a sketchy summary of this well-known text Boyle, Lavoisier, and Curie in chemistry; need be given here. -
Herbert Robbins 1915–2001
Herbert Robbins 1915–2001 A Biographical Memoir by Tze Leung Lai and David Siegmund ©2018 National Academy of Sciences. Any opinions expressed in this memoir are those of the authors and do not necessarily reflect the views of the National Academy of Sciences. HERBERT ELLIS ROBBINS January 12, 1915–February 12, 2001 Elected to the NAS, 1974 Herbert Robbins was widely recognized as one of the most creative and influential mathematical statisticians from 1950 to the turn of the millennium. Entering the then- nascent field of statistics serendipitously in the 1940s, he showed daring originality in three major innovations, all of which occurred during 1951–1956: (a) Compound statistical decision theory and empirical Bayes (b) Sequential design of experiments and multi-armed bandits Book and Manuscript Library, Rare University Archives, Photo courtesy New York. Columbia University in the city of (c) Stochastic approximation and recursive algorithms in stochastic optimization and control. By Tze Leung Lai and David Siegmund Although the importance of these contributions was immediately recognized by Robbins’ contemporaries in statistics and control engineering, their full impacts were not realized until the big-data era in science and technology that arose after his death. Born in New Castle, PA, Robbins entered Harvard University in 1931, at the age of 16. Although his interests until then had been predominantly literary, he found himself increasingly attracted to mathematics through the influence of Marston Morse, who during many long conversations conveyed a vivid sense of the intellectual challenge of creative work in that field. Robbins received the A.B. summa cum laude in 1935, and his Ph.D. -
A Conversation with Sujit Kumar Mitra in 1993 and Some Comments on His Research Publications Simo Puntanen & George P
A conversation with Sujit Kumar Mitra in 1993 and some comments on his research publications Simo Puntanen & George P. H. Styan University of Tampere ISBN 951-44-6784-1 Department of Mathematics, Statistics and Philosophy ISSN 1456-3134 Report A 372 December 2006 A conversation with Sujit Kumar Mitra in 1993 and some comments on his research publications Simo Puntanen Department of Mathematics, Statistics & Philosophy FI-33014 University of Tampere, Finland email: simo.puntanen@uta.fi George P. H. Styan Department of Mathematics and Statistics McGill University 805 ouest rue Sherbrooke Street West Montréal (Québec), Canada H3A 2K6 email: [email protected] Report A 372 Dept. of Mathematics, Statistics & Philosophy December 2006 FI-33014 University of Tampere ISBN 951-44-6784-1 FINLAND ISSN 1456-3134 Photo on the front cover (Simo Puntanen): Sujit Kumar Mitra, New Delhi, December 1992. Photo on the back cover (Simo Puntanen): Sunset in the Hooghly River, Calcutta, December 1994. This Report A 372 is a preliminary edition of “A conversation with Sujit Kumar Mitra in 1993 and some comments on his research publications”. Tampereen Yliopistopaino Oy December 2006 Contents Sujit Kumar Mitra (1932–2004) 5 A conversation with Sujit Kumar Mitra, February 1993 6 Early years, Orissa, 1932–1945 . 7 St. Paul’s College & Presidency College in Calcutta, 1945–1951 9 ISI, Calcutta, 1952–1954 . 16 Ph.D. in Chapel Hill, North Carolina, 1954–1956 . 17 Professor P. C. Mahalanobis in New York City, 1956 . 20 Getting married, 1956–1958 . 24 Back at ISI, Calcutta, 1956–1971, Classic Book 1971 . 27 ISI, New Delhi, 1971–1992 . -
Probability Sampling Designs: Principles for Choice of Design and Balancing
STATISTICAL SCIENCE Volume 32, Number 2 May 2017 Special Section on Complex Surveys Introduction to the Design and Analysis of Complex Survey Data ..........................................................Chris Skinner and Jon Wakefield 165 Probability Sampling Designs: Principles for Choice of Design and Balancing ...........................................................Yves Tillé and Matthieu Wilhelm 176 Model-Assisted Survey Estimation with Modern Prediction Techniques ........................................................ F. Jay Breidt and Jean D. Opsomer 190 ConstructionofWeightsinSurveys:AReview.................................David Haziza and Jean-François Beaumont 206 ApproachestoImprovingSurvey-WeightedEstimates.....Qixuan Chen, Michael R. Elliott, David Haziza, Ye Yang, Malay Ghosh, Roderick J. A. Little, Joseph Sedransk and Mary Thompson 227 Inference for Nonprobability Samples . .............Michael R. Elliott and Richard Valliant 249 FittingRegressionModelstoSurveyData...............Thomas Lumley and Alastair Scott 265 OptionsforConductingWebSurveys................Matthias Schonlau and Mick P. Couper 279 Combining Survey Data with Other Data Sources ...........................................Sharon L. Lohr and Trivellore E. Raghunathan 293 General Section J. B. S. Haldane’s Contribution to the Bayes Factor Hypothesis Test .................................................Alexander Etz and Eric-Jan Wagenmakers 313 Statistical Science [ISSN 0883-4237 (print); ISSN 2168-8745 (online)], Volume 32, Number 2, May 2017. Published