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L.M. Ryan 23321.4 Gender issuesI’ve thought a lot over the years about the issue of gender in our field of statistics.During my almost thirty years in US academia, I was never particularlyconscious of experiencing any obvious bias or discrimination because I was awoman. I was even beginning to think that the days were over where specialefforts were still needed to encourage and support women in science. In fact,IevenfeltslightlyguiltywhenIreceivedtheCOPSSawardthatrecognizesElizabeth L. Scott’s lifelong efforts in the furtherance of the careers of women.Since returning to Australia in early 2009, however, my thinking has changedabit.I’vefoundtheresearchenvironmentheremuchhardertonavigatethanin the US and my confidence has suffered as a result. At a meeting of theAustralian Academy of Science earlier this year, I had something of a lightbulbmoment talking with Terry Speed and several others who assured methat the problem wasn’t just me, but rather I was experiencing the impact ofworking in an environment that was inherently more difficult for women thanfor men. A telling symptom of this was that none of the 20 new fellows electedto the Australian Academy of Science in 2013 were women! While this situationwas something of an embarrassment to the Academy, it did provide animportant opportunity for collective self reflection and dialogue on the issueof gender diversity in Australian science. I realized that I was struggling withsome of the same challenges that I had worked so hard years earlier to helpmy students overcome. Because there were fewer successful academic womenin Australia, I felt more isolated. Also, because the guidelines for assessingsuccess reflected a more male perspective, I was not measuring up so well. Forexample, because of some family responsibilities, I was generally reluctant toaccept many invitations to speak at international conferences. However, suchactivities were seen as very important when it came to evidence of track recordfor grant applications. Finally, my interests didn’t quite align. In the US, I hadbeen very fortunate to spend my career in an environment that embraced interdisciplinaryresearch and where the model of a biostatistician combiningcollaborative and methodological research was not only well understood, butseen as an ideal. In Australia, the model was a more traditional one of a successful,independent academic heading up a team of students, postdocs andjunior staff. For me, this model just didn’t fit. For all these reasons, it madesense that I was having some difficulty in finding my place within the Australianacademic environment. But instead of seeing this bigger picture, I waspersonalizing it and starting to believe that I simply didn’t have the talent tosucceed. I see now that I have an opportunity to put into practice some of theadvice I used to give my students about believing in myself, keeping in mindthe bigger picture and understanding that by persevering, I can help changethe system. My experience also underscores why having a diverse workforcehelps the whole system to be healthier and more effective. A diverse work-
234 Reflections on diversityforce means a diversity of opinions and values, and a diversity of approachesto problem solving. Diversity broadens the scope of what’s important, howworkplaces are organized and how people are valued. In the end, a diverseworkplace is good for everyone.ReferencesCohodas, N. (1997). The Band Played Dixie: Race and the Liberal Conscienceat Ole Miss. FreePress.Gladwell, M. (2011). Outliers: The Story of Success. BackBayBooks,NewYork.Steele, C.M. (2011). Whistling Vivaldi: How Stereotypes Affect Us and WhatWe Can Do. W.W. Norton & Co, New York.
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ContentsPrefacexviiContributorsI Th
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viii15 We live in exciting times 15
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x24.6 Open areas for research . . .
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xii36 Buried treasures 405Michael A
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xiv45.3 Multi-phase inference . . .
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PrefaceStatistics is the science of
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PrefacexixPart V comprises seven ar
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xxiiNancy FlournoyUniversity of Mis
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Part IThe history of COPSS
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4 A brief history of COPSSOnchiota
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6 A brief history of COPSSThe items
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8 A brief history of COPSSTABLE 1.1
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10 A brief history of COPSSwomen of
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12 A brief history of COPSSTABLE 1.
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16 A brief history of COPSS1.4.3 Ge
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20 A brief history of COPSSFor more
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2Reminiscences of the Columbia Univ
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I. Olkin 25Bechhofer, Allan Birnbau
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I. Olkin 27Milton Sobel was the tea
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3AcareerinstatisticsHerman Chernoff
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H. Chernoff 31given three-day basic
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H. Chernoff 33Savage tried to defen
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H. Chernoff 35In working on an arti
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H. Chernoff 37measure the loss as t
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H. Chernoff 39ReferencesAlbert, A.E
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4“. . . how wonderful the field o
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D.R. Brillinger 43He went on to be
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D.R. Brillinger 45Tukey wrote many
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D.R. Brillinger 4718. Personal comm
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50 Unorthodox journey to statistics
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6Statistics before and after my COP
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P.J. Bickel 616.3.1 Imperial Colleg
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P.J. Bickel 63make. This was first
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P.J. Bickel 65We eventually develop
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P.J. Bickel 67school interest, biol
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P.J. Bickel 69ReferencesBean, D., B
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P.J. Bickel 71Meinshausen, N., Bick
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74 Accidental biostatistics profess
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84 Passion for statisticsme is its
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86 Passion for statisticsTheir Biom
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88 Passion for statisticsthe first
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90 Passion for statisticsIn my expe
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92 Passion for statistics8.5 Job an
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94 Passion for statisticsperiod. An
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96 Passion for statisticsNeyman, J.
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98 Reflections on a statistical car
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100 Reflections on a statistical ca
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110 Science mixes it up with statis
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11Lessons from a twisted career pat
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J.S. Rosenthal 119My math professor
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J.S. Rosenthal 121contrary, it null
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J.S. Rosenthal 123of statistical re
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J.S. Rosenthal 125be open to whatev
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J.S. Rosenthal 12711.4 Final though
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130 Promoting equityand fellowship
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132 Promoting equityevaluate such e
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134 Promoting equityfor equity. But
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136 Promoting equityAssistant Secre
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Part IIIPerspectives on the fieldan
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142 Statistics in service to the na
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154 Where are the majors? ArtBachel
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156 Where are the majors?References
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158 Exciting timesCritically, Efron
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160 Exciting timesIn general I find
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162 Exciting timesI entered the Uni
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164 Exciting times15.3.3 Joining th
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166 Exciting timesvariability of a
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168 Exciting timesHabemma, J.D.F.,
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16The bright future of applied stat
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R.A. Irizarry 173FIGURE 16.1Illustr
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R.A. Irizarry 17516.4 The bright fu
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17The road travelled: From statisti
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N. Chatterjee 179the Kaplan-Meyer e
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N. Chatterjee 18117.4 Genome-wide a
Page 204 and 205: N. Chatterjee 183by any means. I ju
Page 206 and 207: N. Chatterjee 185right information
Page 208: N. Chatterjee 187The risk of cancer
Page 211 and 212: 190 Journey into genetics and genom
Page 224 and 225: 19Reflections on women in statistic
Page 226 and 227: M.E. Thompson 205Pearson in England
Page 228 and 229: M.E. Thompson 207TABLE 19.1The 14 C
Page 230 and 231: M.E. Thompson 209time series, the s
Page 232 and 233: M.E. Thompson 211the founding Chair
Page 234 and 235: M.E. Thompson 21319.7 The current s
Page 236: M.E. Thompson 215Yarmie, A. (2003).
Page 239 and 240: 218 “The whole women thing”seem
Page 241 and 242: 220 “The whole women thing”of t
Page 243 and 244: 222 “The whole women thing”on a
Page 245 and 246: 224 “The whole women thing”20.5
Page 247 and 248: 226 “The whole women thing”Ifin
Page 249 and 250: 228 “The whole women thing”Lond
Page 251 and 252: 230 Reflections on diversityas a st
Page 253: 232 Reflections on diversityall of
Page 258 and 259: 22Why does statistics have two theo
Page 260 and 261: D.A.S. Fraser 23922.2 65 years and
Page 262 and 263: D.A.S. Fraser 241function is taken
Page 264 and 265: D.A.S. Fraser 24322.4 Inference for
Page 266 and 267: D.A.S. Fraser 245that record the ef
Page 268 and 269: D.A.S. Fraser 247variable that give
Page 270 and 271: D.A.S. Fraser 249simple scalar case
Page 272 and 273: D.A.S. Fraser 251DiCiccio, T.J. and
Page 274 and 275: 23Conditioning is the issueJames O.
Page 276 and 277: J.O. Berger 255Pedagogical example:
Page 278 and 279: J.O. Berger 257The SRP does not say
Page 280 and 281: J.O. Berger 25923.5 Conditional fre
Page 282 and 283: J.O. Berger 261conditional error pr
Page 284 and 285: J.O. Berger 263others). If x is the
Page 286 and 287: J.O. Berger 265How does a frequenti
Page 288 and 289: 24Statistical inference from aDemps
Page 290 and 291: A.P. Dempster 269tion is simply une
Page 292 and 293: A.P. Dempster 271As the world of st
Page 294 and 295: A.P. Dempster 273when the X i are a
Page 296 and 297: A.P. Dempster 27524.5 Nonparametric
Page 298 and 299: A.P. Dempster 277can easily happen
Page 300 and 301: A.P. Dempster 279ReferencesBorel,
Page 302 and 303: 25Nonparametric BayesDavid B. Dunso
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D.B. Dunson 283troduced, providing
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D.B. Dunson 285els in spatial stati
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D.B. Dunson 287methods as also prov
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D.B. Dunson 289ing background, and
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D.B. Dunson 291Murray, J.S., Dunson
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294 Choosing default methodsimagine
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296 Choosing default methods(a) mat
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298 Choosing default methodspartial
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300 Choosing default methodsdirecti
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27Serial correlation and Durbin-Wat
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T.W. Anderson 305The characteristic
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T.W. Anderson 307Consider the seria
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28Anon-asymptoticwalkinprobabilitya
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P. Massart 311(at least for discret
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P. Massart 31328.2.2 Non-asymptopia
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P. Massart 315where P = sµ. In oth
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P. Massart 317Hence by Talagrand’
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P. Massart 319ReferencesAkaike, H.
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P. Massart 321Talagrand, M. (1995).
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324 The present’s futuredata. In
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326 The present’s futureTABLE 29.
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328 The present’s future124356981
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330 The present’s futuremethod, t
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332 The present’s futureFIGURE 29
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334 The present’s futureIchino, M
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336 Lessons in biostatistics30.2 It
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338 Lessons in biostatisticsprognos
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340 Lessons in biostatisticstime fo
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342 Lessons in biostatisticsmeaning
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344 Lessons in biostatistics75% to
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346 Lessons in biostatisticshistolo
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31AvignetteofdiscoveryNancy Flourno
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N. Flournoy 351FIGURE 31.1Incidence
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N. Flournoy 353FIGURE 31.3Mean resp
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N. Flournoy 355FIGURE 31.4Kaplan-Me
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N. Flournoy 357As the study proceed
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32Statistics and public health rese
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R.L. Prentice 36132.2 Public health
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R.L. Prentice 363from urinary excre
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R.L. Prentice 36532.5 Clinical tria
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R.L. Prentice 367Kaplan, E.L. and M
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370 Statistics in a new erative eff
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372 Statistics in a new eraular com
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374 Statistics in a new erajoint de
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376 Statistics in a new eraof the n
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378 Statistics in a new eraLai, T.L
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34Meta-analyses: Heterogeneity can
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N.M. Laird 383into the sum of a “
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N.M. Laird 385are “approximately
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N.M. Laird 387included in the major
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N.M. Laird 389Yusuf, S., Peto, R.,
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392 Good health: Statistical challe
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406 Buried treasuresmeasurement tec
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408 Buried treasuresIn a most rewar
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410 Buried treasureslems different
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37Survey sampling: Past controversi
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R.J. Little 41537.2 Probability or
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R.J. Little 417for the “design-ba
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R.J. Little 419estimate of variance
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R.J. Little 421Example 2 continued
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R.J. Little 42337.3.4 The design-mo
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R.J. Little 42537.5 ConclusionsI am
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R.J. Little 427Isaki, C.T. and Full
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38Environmental informatics: Uncert
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N. Cressie 431and conquer” strate
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N. Cressie 433In the last 20 years,
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N. Cressie 435Once the satellite ha
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N. Cressie 437FIGURE 38.1Locations
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N. Cressie 439regional and seasonal
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N. Cressie 441ciated with the spati
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N. Cressie 443is the set of all pix
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N. Cressie 445challenge is to devel
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N. Cressie 447Gourdji, S.M., Muelle
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N. Cressie 449Wikle, C.K., Milliff,
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452 Statistical geneticsTABLE 39.1T
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454 Statistical geneticsStewart, 19
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456 Statistical geneticslocidata
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458 Statistical geneticssufficientl
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460 Statistical geneticsBrown, M.D.
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462 Statistical geneticsLange, K. a
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464 Statistical geneticsThompson, E
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466 Targeted learningods for nonpar
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468 Targeted learningdenote the obs
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470 Targeted learningSuch an estima
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472 Targeted learningliterature pro
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474 Targeted learninglihood estimat
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476 Targeted learning40.6 Some spec
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478 Targeted learningmemory challen
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480 Targeted learningvan der Laan,
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482 Ah-ha momentwe have just seen.
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484 Ah-ha momentthe data is unchang
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486 Ah-ha momentis well defined and
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488 Ah-ha momentit was something of
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490 Ah-ha momentdefines a Euclidean
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492 Ah-ha momentparticipate in the
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494 Ah-ha momentLee, Y., Wahba, G.,
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42In praise of sparsity and convexi
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R.J. Tibshirani 499TABLE 42.1Asampl
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R.J. Tibshirani 501CancerEpithelial
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R.J. Tibshirani 503that the error v
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R.J. Tibshirani 505Tibshirani, R.J.
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508 Features of Big Dataproblems en
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510 Features of Big Datalearning st
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512 Features of Big Data1210(a)p =
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514 Features of Big Data25002000150
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516 Features of Big DataAs an illus
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518 Features of Big Datafor some gi
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520 Features of Big Data43.7.3 Prop
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522 Features of Big DataBühlmann,
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44Rise of the machinesLarry A. Wass
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L.A. Wasserman 527if an idea requir
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L.A. Wasserman 529●●●● ●
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L.A. Wasserman 531●●●●●
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L.A. Wasserman 533FIGURE 44.4Simula
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L.A. Wasserman 53544.7 Education an
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45Atrioofinferenceproblemsthatcould
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X.-L. Meng 539it is directly rooted
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X.-L. Meng 541exactly, but this doe
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X.-L. Meng 543That is, when we do n
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X.-L. Meng 545(a) For what classes
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X.-L. Meng 547subsequent analysts c
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X.-L. Meng 549can prove only that (
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X.-L. Meng 551In general, “What t
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X.-L. Meng 553vironmental progress
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X.-L. Meng 555which will be compare
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X.-L. Meng 557(a) Given partial kno
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X.-L. Meng 559AcknowledgementsThe m
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X.-L. Meng 561Knuth, D. (1997). The
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Part VAdvice for the nextgeneration
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566 Inspiration, aspiration, ambiti
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568 Inspiration, aspiration, ambiti
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47Personal reflections on the COPSS
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R.J. Carroll 573them is, by definit
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R.J. Carroll 575(Carroll, 2003). In
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R.J. Carroll 57747.8 After the Pres
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R.J. Carroll 579Carroll, R.J., Spie
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582 Publishing without perishingthe
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584 Publishing without perishingbeg
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586 Publishing without perishingme,
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588 Publishing without perishingalr
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590 Publishing without perishingpor
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49Converting rejections into positi
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D.B. Rubin 595This pair of submissi
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D.B. Rubin 597There are several les
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D.B. Rubin 599very sound article, w
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D.B. Rubin 60149.8 ConclusionIhaveb
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D.B. Rubin 603Rubin, D.B. (1972). A
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606 Importance of mentorstwice as t
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608 Importance of mentorsexperiment
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610 Importance of mentorsposition a
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612 Importance of mentors50.7 The t
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51Never ask for or give advice, mak
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T. Speed 617overly generous. Perhap
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T. Speed 619and for finding enjoyme
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622 Thirteen rules3. Waste a lot of
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