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E-raamat: Past, Present, and Future of Statistical Science

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Edited by (Harvard School of Public Health, Boston, Massachusetts, USA), Edited by , Edited by (University of California, Davis, USA), Edited by (Universiteit Hasselt & KU Leuven, Belgium), Edited by (Duke Univ), Edited by (McGill University, Montréal (Québec), Canada)
  • Formaat: 646 pages
  • Ilmumisaeg: 26-Mar-2014
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482204988
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Past, Present, and Future of Statistical ScienceSuurem pilt
  • Formaat: 646 pages
  • Ilmumisaeg: 26-Mar-2014
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482204988
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Past, Present, and Future of Statistical Science was commissioned in 2013 by the Committee of Presidents of Statistical Societies (COPSS) to celebrate its 50th anniversary and the International Year of Statistics. COPSS consists of five charter member statistical societies in North America and is best known for sponsoring prestigious awards in statistics, such as the COPSS Presidents award.

Through the contributions of a distinguished group of 50 statisticians who are past winners of at least one of the five awards sponsored by COPSS, this volume showcases the breadth and vibrancy of statistics, describes current challenges and new opportunities, highlights the exciting future of statistical science, and provides guidance to future generations of statisticians. The book is not only about statistics and science but also about people and their passion for discovery.

Distinguished authors present expository articles on a broad spectrum of topics in statistical education, research, and applications. Topics covered include reminiscences and personal reflections on statistical careers, perspectives on the field and profession, thoughts on the discipline and the future of statistical science, and advice for young statisticians. Many of the articles are accessible not only to professional statisticians and graduate students but also to undergraduate students interested in pursuing statistics as a career and to all those who use statistics in solving real-world problems. A consistent theme of all the articles is the passion for statistics enthusiastically shared by the authors. Their success stories inspire, give a sense of statistics as a discipline, and provide a taste of the exhilaration of discovery, success, and professional accomplishment.

Arvustused

"This collection of reminiscences, musings on the state of the art, and advice for young statisticians makes for compelling reading. There are 52 contributions from eminent statisticians who have won a Committee of Presidents of Statistical Societies award. Each is a short, focused chapter and so one could even say this is ideal bedtime (or coffee break) reading. Anyone interested in the history of statistics will know that much has been written about the early days but little about the field since the Second World War. This book goes some way to redress this and is all the more valuable for coming from the horses mouththe closing chapter, the shortest of all, from Brad Efron: a list of "thirteen rules for giving a really bad talk". This made me laugh out loud and should be posted on the walls of all conferences. I shall leave the final word to Peter Bickel: "We should glory in this time when statistical thinking pervades almost every field of endeavor. It is really a lot of fun." Robert Grant, in Significance, April 2017

"This volume captures a broad range of views on what makes statistics interestingwith a focus that ranges across the mathematics of statistics, its partnership with computing, the role of statistics in science, the insights that it can provide into diverse areas of science, and the competing philosophies that underpin statistical practice. The contributions vary widely in their sophistication, from those that a first-year undergraduate may read and find interesting to those that make strong mathematical and/or statistical demands." International Statistical Review, 2015

"This work celebrates the 50th anniversary of the Committee of Presidents of Statistical Societies (COPSS) and the International Year of Statistics. ... Each contributing author is a past winner of a COPSS-sponsored award. This engaging, informative book will be useful for students and researchers planning on entering careers in statistics. Highly recommended." CHOICE, May 2015

" must-read for statisticians and provide[ s] valuable insight for people who are training the next generation of statistical professionals The book features 50 chapter authors, each of whom is a recipient of at least one of the awards sponsored by COPSS. It is a distinguished list of contributors, and not surprisingly, the book does not disappoint. The COPSS book explores this present vibrancy and vitality across a range of topics that reflect the vast diversity of statistical practice. What is particularly useful about this bookin my viewis the documentation of the stories of some statisticians, stories that have relevance for all of us, but perhaps especially for those new to the profession. Indeed, for those new to our profession, the COPSS book presents an entire section directed to them titled Advice for the Next Generation. the book contains some insightful nuggets about the future. Fifty great thinkers about statistics provide the reader of the COPSS book with reminiscences to learn from, technical questions to tackle, and challenges that inspire." The American Statistician, February 2015

"Nat Schenker, president of the American Statistical Association, had this to say about Past, Present, and Future of Statistical Science: It is a veritable feast, a 50-course buffet prepared by many of the most distinguished statisticians of our day. Enjoyable eating from start to end, and good for you, too." Significance Recommended Reading, December 2014

Preface xvii
Contributors xxi
I The history of COPSS
1(20)
1 A brief history of the Committee of Presidents of Statistical Societies (COPSS)
3(18)
Ingram Olkin
1.1 Introduction
3(3)
1.2 COPSS activities in the early years
6(2)
1.3 COPSS activities in recent times
8(2)
1.4 Awards
10(11)
II Reminiscences and personal reflections on career paths
21(118)
2 Reminiscences of the Columbia University Department of Mathematical Statistics in the late 1940s
23(6)
Ingram Olkin
2.1 Introduction: Pre-Columbia
23(1)
2.2 Columbia days
24(2)
2.3 Courses
26(3)
3 A career in statistics
29(12)
Herman Chernoff
3.1 Education
29(3)
3.2 Postdoc at University of Chicago
32(2)
3.3 University of Illinois and Stanford
34(4)
3.4 MIT and Harvard
38(3)
4 "... how wonderful the field of statistics is ..."
41(8)
David R. Brillinger
4.1 Introduction
41(1)
4.2 The speech (edited some)
42(3)
4.3 Conclusion
45(4)
5 An unorthodox journey to statistics: Equity issues, remarks on multiplicity
49(10)
Juliet Popper Shaffer
5.1 Pre-statistical career choices
49(1)
5.2 Becoming a statistician
50(2)
5.3 Introduction to and work in multiplicity
52(2)
5.4 General comments on multiplicity
54(5)
6 Statistics before and after my COPSS Prize
59(14)
Peter J. Bickel
6.1 Introduction
59(1)
6.2 The foundation of mathematical statistics
59(1)
6.3 My work before 1979
60(2)
6.4 My work after 1979
62(5)
6.5 Some observations
67(6)
7 The accidental biostatistics professor
73(10)
Donna J. Brogan
7.1 Public school and passion for mathematics
73(1)
7.2 College years and discovery of statistics
74(2)
7.3 Thwarted employment search after college
76(1)
7.4 Graduate school as a fallback option
76(1)
7.5 Master's degree in statistics at Purdue
77(1)
7.6 Thwarted employment search after Master's degree
77(1)
7.7 Graduate school again as a fallback option
77(1)
7.8 Dissertation research and family issues
78(1)
7.9 Job offers --- finally!
79(1)
7.10 Four years at UNC-Chapel Hill
79(1)
7.11 Thirty-three years at Emory University
80(1)
7.12 Summing up and acknowledgements
81(2)
8 Developing a passion for statistics
83(14)
Bruce G. Lindsay
8.1 Introduction
83(2)
8.2 The first statistical seeds
85(1)
8.3 Graduate training
85(3)
8.4 The PhD
88(4)
8.5 Job and postdoc hunting
92(1)
8.6 The postdoc years
92(1)
8.7 Starting on the tenure track
93(4)
9 Reflections on a statistical career and their implications
97(12)
R. Dennis Cook
9.1 Early years
97(3)
9.2 Statistical diagnostics
100(4)
9.3 Optimal experimental design
104(1)
9.4 Enjoying statistical practice
105(1)
9.5 A lesson learned
106(3)
10 Science mixes it up with statistics
109(8)
Kathryn Roeder
10.1 Introduction
109(1)
10.2 Collaborators
110(1)
10.3 Some collaborative projects
111(3)
10.4 Conclusions
114(3)
11 Lessons from a twisted career path
117(12)
Jeffrey S. Rosenthal
11.1 Introduction
117(1)
11.2 Student days
118(4)
11.3 Becoming a researcher
122(5)
11.4 Final thoughts
127(2)
12 Promoting equity
129(10)
Mary W. Gray
12.1 Introduction
129(1)
12.2 The Elizabeth Scott Award
130(2)
12.3 Insurance
132(2)
12.4 Title IX
134(1)
12.5 Human rights
134(2)
12.6 Underrepresented groups
136(3)
III Perspectives on the field and profession
139(96)
13 Statistics in service to the nation
141(12)
Stephen E. Fienberg
13.1 Introduction
141(2)
13.2 The National Halothane Study
143(1)
13.3 The President's Commission and CNSTAT
144(1)
13.4 Census-taking and multiple-systems estimation
145(1)
13.5 Cognitive aspects of survey methodology
146(1)
13.6 Privacy and confidentiality
147(1)
13.7 The accuracy of the polygraph
148(1)
13.8 Take-home messages
149(4)
14 Where are the majors?
153(4)
Iain M. Johnstone
14.1 The puzzle
153(1)
14.2 The data
153(1)
14.3 Some remarks
154(3)
15 We live in exciting times
157(14)
Peter G. Hall
15.1 Introduction
157(2)
15.2 Living with change
159(2)
15.3 Living the revolution
161(10)
16 The bright future of applied statistics
171(6)
Rafael A. Irizarry
16.1 Introduction
171(1)
16.2 Becoming an applied statistician
171(1)
16.3 Genomics and the measurement revolution
172(3)
16.4 The bright future
175(2)
17 The road travelled: From statistician to statistical scientist
177(12)
Nilanjan Chatterjee
17.1 Introduction
177(1)
17.2 Kin-cohort study: My gateway to genetics
178(1)
17.3 Gene-environment interaction: Bridging genetics and theory of case-control studies
179(2)
17.4 Genome-wide association studies (GWAS): Introduction to big science
181(2)
17.5 The post-GWAS era: What does it all mean?
183(1)
17.6 Conclusion
184(5)
18 A journey into statistical genetics and genomics
189(14)
Xihong Lin
18.1 The 'omics era
189(2)
18.2 My move into statistical genetics and genomics
191(1)
18.3 A few lessons learned
192(1)
18.4 A few emerging areas in statistical genetics and genomics
193(4)
18.5 Training the next generation statistical genetic and genomic scientists in the 'omics era
197(2)
18.6 Concluding remarks
199(4)
19 Reflections on women in statistics in Canada
203(14)
Mary E. Thompson
19.1 A glimpse of the hidden past
203(1)
19.2 Early historical context
204(2)
19.3 A collection of firsts for women
206(3)
19.4 Awards
209(1)
19.5 Builders
210(2)
19.6 Statistical practice
212(1)
19.7 The current scene
213(4)
20 "The whole women thing"
217(12)
Nancy M. Reid
20.1 Introduction
217(1)
20.2 "How many women are there in your department?"
218(2)
20.3 "Should I ask for more money?"
220(1)
20.4 "I'm honored"
221(3)
20.5 "I loved that photo"
224(1)
20.6 Conclusion
225(4)
21 Reflections on diversity
229(6)
Louise M. Ryan
21.1 Introduction
229(1)
21.2 Initiatives for minority students
230(1)
21.3 Impact of the diversity programs
231(2)
21.4 Gender issues
233(2)
IV Reflections on the discipline
235(328)
22 Why does statistics have two theories?
237(16)
Donald A.S. Fraser
22.1 Introduction
237(2)
22.2 65 years and what's new
239(1)
22.3 Where do the probabilities come from?
240(3)
22.4 Inference for regular models: Frequency
243(2)
22.5 Inference for regular models: Bootstrap
245(1)
22.6 Inference for regular models: Bayes
246(1)
22.7 The frequency-Bayes contradiction
247(1)
22.8 Discussion
248(5)
23 Conditioning is the issue
253(14)
James O. Berger
23.1 Introduction
253(1)
23.2 Cox example and a pedagogical example
254(1)
23.3 Likelihood and stopping rule principles
255(2)
23.4 What it means to be a frequentist
257(2)
23.5 Conditional frequentist inference
259(5)
23.6 Final comments
264(3)
24 Statistical inference from a Dempster--Shafer perspective
267(14)
Arthur P. Dempster
24.1 Introduction
267(1)
24.2 Personal probability
268(1)
24.3 Personal probabilities of "don't know"
269(2)
24.4 The standard DS protocol
271(4)
24.5 Nonparametric inference
275(1)
24.6 Open areas for research
276(5)
25 Nonparametric Bayes
281(12)
David B. Dunson
25.1 Introduction
281(3)
25.2 A brief history of NP Bayes
284(3)
25.3 Gazing into the future
287(6)
26 How do we choose our default methods?
293(10)
Andrew Gelman
26.1 Statistics: The science of defaults
293(2)
26.2 Ways of knowing
295(2)
26.3 The pluralist's dilemma
297(2)
26.4 Conclusions
299(4)
27 Serial correlation and Durbin--Watson bounds
303(6)
T.W. Anderson
27.1 Introduction
303(1)
27.2 Circular serial correlation
304(1)
27.3 Periodic trends
305(1)
27.4 Uniformly most powerful tests
305(1)
27.5 Durbin--Watson
306(3)
28 A non-asymptotic walk in probability and statistics
309(14)
Pascal Massart
28.1 Introduction
309(1)
28.2 Model selection
310(5)
28.3 Welcome to Talagrand's wonderland
315(3)
28.4 Beyond Talagrand's inequality
318(5)
29 The past's future is now: What will the present's future bring?
323(12)
Lynne Billard
29.1 Introduction
323(1)
29.2 Symbolic data
324(1)
29.3 Illustrations
325(6)
29.4 Conclusion
331(4)
30 Lessons in biostatistics
335(14)
Norman E. Breslow
30.1 Introduction
335(1)
30.2 It's the science that counts
336(2)
30.3 Immortal time
338(3)
30.4 Multiplicity
341(4)
30.5 Conclusion
345(4)
31 A vignette of discovery
349(10)
Nancy Flournoy
31.1 Introduction
349(1)
31.2 CMV infection and clinical pneumonia
350(4)
31.3 Interventions
354(3)
31.4 Conclusions
357(2)
32 Statistics and public health research
359(10)
Ross L. Prentice
32.1 Introduction
359(2)
32.2 Public health research
361(1)
32.3 Biomarkers and nutritional epidemiology
362(1)
32.4 Preventive intervention development and testing
363(2)
32.5 Clinical trial data analysis methods
365(1)
32.6 Summary and conclusion
365(4)
33 Statistics in a new era for finance and health care
369(12)
Tze Leung Lai
33.1 Introduction
369(1)
33.2 Comparative effectiveness research clinical studies
370(1)
33.3 Innovative clinical trial designs in translational medicine
371(2)
33.4 Credit portfolios and dynamic empirical Bayes in finance
373(2)
33.5 Statistics in the new era of finance
375(1)
33.6 Conclusion
376(5)
34 Meta-analyses: Heterogeneity can be a good thing
381(10)
Nan M. Laird
34.1 Introduction
381(1)
34.2 Early years of random effects for meta-analysis
382(1)
34.3 Random effects and clinical trials
383(2)
34.4 Meta-analysis in genetic epidemiology
385(2)
34.5 Conclusions
387(4)
35 Good health: Statistical challenges in personalizing disease prevention
391(14)
Alice S. Whittemore
35.1 Introduction
391(1)
35.2 How do we personalize disease risks?
391(2)
35.3 How do we evaluate a personal risk model?
393(1)
35.4 How do we estimate model performance measures?
394(3)
35.5 Can we improve how we use epidemiological data for risk model assessment?
397(4)
35.6 Concluding remarks
401(4)
36 Buried treasures
405(8)
Michael A. Newton
36.1 Three short stories
405(4)
36.2 Concluding remarks
409(4)
37 Survey sampling: Past controversies, current orthodoxy, and future paradigms
413(16)
Roderick J.A. Little
37.1 Introduction
413(2)
37.2 Probability or purposive sampling?
415(1)
37.3 Design-based or model-based inference?
416(7)
37.4 A unified framework: Calibrated Bayes
423(2)
37.5 Conclusions
425(4)
38 Environmental informatics: Uncertainty quantification in the environmental sciences
429(22)
Noel Cressie
38.1 Introduction
429(1)
38.2 Hierarchical statistical modeling
430(1)
38.3 Decision-making in the presence of uncertainty
431(2)
38.4 Smoothing the data
433(1)
38.5 EI for spatio-temporal data
434(10)
38.6 The knowledge pyramid
444(1)
38.7 Conclusions
444(7)
39 A journey with statistical genetics
451(14)
Elizabeth A. Thompson
39.1 Introduction
451(1)
39.2 The 1970s: Likelihood inference and the EM algorithm
452(2)
39.3 The 1980s: Genetic maps and hidden Markov models
454(1)
39.4 The 1990s: MCMC and complex stochastic systems
455(2)
39.5 The 2000s: Association studies and gene expression
457(1)
39.6 The 2010s: From association to relatedness
458(1)
39.7 To the future
458(7)
40 Targeted learning: From MLE to TMLE
465(16)
Mark van der Laan
40.1 Introduction
465(2)
40.2 The statistical estimation problem
467(2)
40.3 The curse of dimensionality for the MLE
469(4)
40.4 Super learning
473(1)
40.5 Targeted learning
474(2)
40.6 Some special topics
476(1)
40.7 Concluding remarks
477(4)
41 Statistical model building, machine learning, and the ah-ha moment
481(16)
Grace Wahba
41.1 Introduction: Manny Parzen and RKHS
481(9)
41.2 Regularization methods, RKHS and sparse models
490(1)
41.3 Remarks on the nature-nurture debate, personalized medicine and scientific literacy
491(1)
41.4 Conclusion
492(5)
42 In praise of sparsity and convexity
497(10)
Robert J. Tibshirani
42.1 Introduction
497(1)
42.2 Sparsity, convexity and l1 penalties
498(2)
42.3 An example
500(1)
42.4 The covariance test
500(3)
42.5 Conclusion
503(4)
43 Features of Big Data and sparsest solution in high confidence set
507(18)
Jianqing Fan
43.1 Introduction
507(1)
43.2 Heterogeneity
508(1)
43.3 Computation
509(1)
43.4 Spurious correlation
510(2)
43.5 Incidental endogeneity
512(3)
43.6 Noise accumulation
515(1)
43.7 Sparsest solution in high confidence set
516(5)
43.8 Conclusion
521(4)
44 Rise of the machines
525(12)
Larry A. Wasserman
44.1 Introduction
525(1)
44.2 The conference culture
526(1)
44.3 Neglected research areas
527(1)
44.4 Case studies
527(6)
44.5 Computational thinking
533(1)
44.6 The evolving meaning of data
534(1)
44.7 Education and hiring
535(1)
44.8 If you can't beat them, join them
535(2)
45 A trio of inference problems that could win you a Nobel Prize in statistics (if you help fund it)
537(26)
Xiao-Li Meng
45.1 Nobel Prize? Why not COPSS?
537(2)
45.2 Multi-resolution inference
539(6)
45.3 Multi-phase inference
545(6)
45.4 Multi-source inference
551(6)
45.5 The ultimate prize or price
557(6)
V Advice for the next generation
563
46 Inspiration, aspiration, ambition
565(6)
C.F. Jeff Wu
46.1 Searching the source of motivation
565(1)
46.2 Examples of inspiration, aspiration, and ambition
566(1)
46.3 Looking to the future
567(4)
47 Personal reflections on the COPSS Presidents' Award
571(10)
Raymond J. Carroll
47.1 The facts of the award
571(1)
47.2 Persistence
571(1)
47.3 Luck: Have a wonderful Associate Editor
572(1)
47.4 Find brilliant colleagues
572(2)
47.5 Serendipity with data
574(1)
47.6 Get fascinated: Heteroscedasticity
575(1)
47.7 Find smart subject-matter collaborators
575(2)
47.8 After the Presidents' Award
577(4)
48 Publishing without perishing and other career advice
581(12)
Marie Davidian
48.1 Introduction
581(1)
48.2 Achieving balance, and how you never know
582(4)
48.3 Write it, and write it again
586(4)
48.4 Parting thoughts
590(3)
49 Converting rejections into positive stimuli
593(12)
Donald B. Rubin
49.1 My first attempt
594(1)
49.2 I'm learning
594(1)
49.3 My first JASA submission
595(1)
49.4 Get it published!
596(1)
49.5 Find reviewers who understand
597(1)
49.6 Sometimes it's easy, even with errors
598(1)
49.7 It sometimes pays to withdraw the paper!
598(3)
49.8 Conclusion
601(4)
50 The importance of mentors
605(10)
Donald B. Rubin
50.1 My early years
605(1)
50.2 The years at Princeton University
606(2)
50.3 Harvard University --- the early years
608(1)
50.4 My years in statistics as a PhD student
609(1)
50.5 The decade at ETS
610(1)
50.6 Interim time in DC at EPA, at the University of Wisconsin, and the University of Chicago
611(1)
50.7 The three decades at Harvard
612(1)
50.8 Conclusions
612(3)
51 Never ask for or give advice, make mistakes, accept mediocrity, enthuse
615(6)
Terry Speed
51.1 Never ask for or give advice
615(1)
51.2 Make mistakes
616(1)
51.3 Accept mediocrity
617(1)
51.4 Enthuse
618(3)
52 Thirteen rules
621
Bradley Efron
52.1 Introduction
621(1)
52.2 Thirteen rules for giving a really bad talk
621
Xihong Lin, Christian Genest, David L. Banks, Geert Molenberghs, David W. Scott, Jane-Ling Wang
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