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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.

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Table of content:

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

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