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N.M. Laird 389Yusuf, S., Peto, R., Lewis, J., Collins, R., and Sleight, P. (1985). Beta blockadeduring and after myocardial infarction: an overview of the randomizedtrials. Progress in Cardiovascular Diseases, 27:335–371.
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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
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N. Chatterjee 183by any means. I ju
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N. Chatterjee 185right information
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N. Chatterjee 187The risk of cancer
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190 Journey into genetics and genom
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19Reflections on women in statistic
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M.E. Thompson 205Pearson in England
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M.E. Thompson 207TABLE 19.1The 14 C
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M.E. Thompson 209time series, the s
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M.E. Thompson 211the founding Chair
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M.E. Thompson 21319.7 The current s
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M.E. Thompson 215Yarmie, A. (2003).
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218 “The whole women thing”seem
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220 “The whole women thing”of t
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222 “The whole women thing”on a
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224 “The whole women thing”20.5
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226 “The whole women thing”Ifin
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228 “The whole women thing”Lond
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230 Reflections on diversityas a st
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232 Reflections on diversityall of
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234 Reflections on diversityforce m
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22Why does statistics have two theo
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D.A.S. Fraser 23922.2 65 years and
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D.A.S. Fraser 241function is taken
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D.A.S. Fraser 24322.4 Inference for
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D.A.S. Fraser 245that record the ef
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D.A.S. Fraser 247variable that give
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D.A.S. Fraser 249simple scalar case
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D.A.S. Fraser 251DiCiccio, T.J. and
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23Conditioning is the issueJames O.
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J.O. Berger 255Pedagogical example:
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J.O. Berger 257The SRP does not say
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J.O. Berger 25923.5 Conditional fre
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J.O. Berger 261conditional error pr
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J.O. Berger 263others). If x is the
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J.O. Berger 265How does a frequenti
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24Statistical inference from aDemps
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A.P. Dempster 269tion is simply une
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A.P. Dempster 271As the world of st
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A.P. Dempster 273when the X i are a
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A.P. Dempster 27524.5 Nonparametric
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A.P. Dempster 277can easily happen
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A.P. Dempster 279ReferencesBorel,
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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
Page 359 and 360: 338 Lessons in biostatisticsprognos
Page 361 and 362: 340 Lessons in biostatisticstime fo
Page 363 and 364: 342 Lessons in biostatisticsmeaning
Page 365 and 366: 344 Lessons in biostatistics75% to
Page 367 and 368: 346 Lessons in biostatisticshistolo
Page 370 and 371: 31AvignetteofdiscoveryNancy Flourno
Page 372 and 373: N. Flournoy 351FIGURE 31.1Incidence
Page 374 and 375: N. Flournoy 353FIGURE 31.3Mean resp
Page 376 and 377: N. Flournoy 355FIGURE 31.4Kaplan-Me
Page 378 and 379: N. Flournoy 357As the study proceed
Page 380 and 381: 32Statistics and public health rese
Page 382 and 383: R.L. Prentice 36132.2 Public health
Page 384 and 385: R.L. Prentice 363from urinary excre
Page 386 and 387: R.L. Prentice 36532.5 Clinical tria
Page 388: R.L. Prentice 367Kaplan, E.L. and M
Page 391 and 392: 370 Statistics in a new erative eff
Page 393 and 394: 372 Statistics in a new eraular com
Page 395 and 396: 374 Statistics in a new erajoint de
Page 397 and 398: 376 Statistics in a new eraof the n
Page 399 and 400: 378 Statistics in a new eraLai, T.L
Page 402 and 403: 34Meta-analyses: Heterogeneity can
Page 404 and 405: N.M. Laird 383into the sum of a “
Page 406 and 407: N.M. Laird 385are “approximately
Page 408 and 409: N.M. Laird 387included in the major
Page 412 and 413: 35Good health: Statistical challeng
Page 414 and 415: A.S. Whittemore 393form a useful fo
Page 416 and 417: A.S. Whittemore 395variate values.
Page 418 and 419: A.S. Whittemore 397the observed fla
Page 420 and 421: A.S. Whittemore 399represented, for
Page 422 and 423: A.S. Whittemore 401Consider, for ex
Page 424 and 425: A.S. Whittemore 403Venter, D.J., We
Page 426 and 427: 36Buried treasuresMichael A. Newton
Page 428 and 429: M.A. Newton 407FIGURE 36.1The relat
Page 430 and 431: M.A. Newton 409When measured at sev
Page 432: M.A. Newton 411Mason, D.M. and Newt
Page 435 and 436: 414 Survey samplingclustering. Anot
Page 437 and 438: 416 Survey samplingobjective is the
Page 439 and 440: 418 Survey samplingEstimators ̂q a
Page 441 and 442: 420 Survey samplingagainst model mi
Page 443 and 444: 422 Survey samplingwrong, so can we
Page 445 and 446: 424 Survey samplinggood design-base
Page 447 and 448: 426 Survey samplingBox, G.E.P. (198
Page 449 and 450: 428 Survey samplingRubin, D.B. (197
Page 451 and 452: 430 Environmental informaticsHastie
Page 453 and 454: 432 Environmental informaticsthis w
Page 455 and 456: 434 Environmental informaticsthe da
Page 457 and 458: 436 Environmental informaticssatell
Page 459 and 460: 438 Environmental informaticsThe mo
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440 Environmental informaticsThe ch
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442 Environmental informaticsFIGURE
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444 Environmental informatics38.6 T
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446 Environmental informaticserror
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448 Environmental informaticsMcLach
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39AjourneywithstatisticalgeneticsEl
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E.A. Thompson 453PopulationMeiosisG
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E.A. Thompson 455TABLE 39.2The chan
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E.A. Thompson 45739.5 The 2000s: As
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E.A. Thompson 459I have given many
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E.A. Thompson 461Fisher, R.A. (1922
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E.A. Thompson 463Schena, M., Shalon
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40Targeted learning: From MLE to TM
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M. van der Laan 467confidence inter
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M. van der Laan 46940.2.3 Target qu
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M. van der Laan 471the doubly censo
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M. van der Laan 47340.4 Super learn
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M. van der Laan 475has the general
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M. van der Laan 477should also be c
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M. van der Laan 479Petersen, M. and
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41Statistical model building, machi
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G. Wahba 483geometrical argument co
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G. Wahba 485matrix is a rank p proj
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G. Wahba 487as proposed by Vapnik a
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G. Wahba 489details. In Corrada Bra
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G. Wahba 491As is easy to see here
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G. Wahba 493ceedings of the Nationa
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G. Wahba 495Vapnik, V. (1995). The
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498 Sparsity and convexity .^ .^ FI
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500 Sparsity and convexityrecover t
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502 Sparsity and convexity Tes
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504 Sparsity and convexityReference
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43Features of Big Data and sparsest
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J. Fan 509chemotherapy is helpful f
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J. Fan 511Realizations of two indep
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J. Fan 513selection of genes or SNP
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J. Fan 515 65432100.05 0 0.05 0.1
Page 538 and 539:
J. Fan 5174(a) m=210(b) m=100250204
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J. Fan 519where · denotes the comp
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J. Fan 52143.8 ConclusionBig Data a
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J. Fan 523Hall, P., Titterington, D
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526 Rise of the machinesric regress
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528 Rise of the machines●●●
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530 Rise of the machines44.4.2 Case
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532 Rise of the machinesis that the
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534 Rise of the machinesFIGURE 44.5
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536 Rise of the machinesAcknowledge
Page 559 and 560:
538 NP-hard inferenceseem to apprec
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540 NP-hard inferencesignal or nois
Page 563 and 564:
542 NP-hard inferenceIn the stricte
Page 565 and 566:
544 NP-hard inferenceover equivalen
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546 NP-hard inferencelarge to pass
Page 569 and 570:
548 NP-hard inferenceTo illustrate
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550 NP-hard inferencemodel P Y (Y |
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552 NP-hard inferenceand accepted (
Page 575 and 576:
554 NP-hard inferenceHere let us as
Page 577 and 578:
556 NP-hard inferenceThe (middle) r
Page 579 and 580:
558 NP-hard inferenceunderstanding
Page 581 and 582:
560 NP-hard inferenceBouman, P., Me
Page 583 and 584:
562 NP-hard inferenceNason, G.P. (2
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46Inspiration, aspiration, ambition
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C.F.J. Wu 567works, he wrote of Pea
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C.F.J. Wu 569you will become, can p
Page 593 and 594:
572 Personal reflections on the COP
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48Publishing without perishing and
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M. Davidian 583tion research. He wa
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M. Davidian 585it accessible to pra
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M. Davidian 587emphasized the impor
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M. Davidian 589s/he needs to have f
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M. Davidian 591enjoy what you do, a
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594 Converting rejectionsespecially
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596 Converting rejectionsdecent sug
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598 Converting rejectionssay, espec
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600 Converting rejectionsfinal anal
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602 Converting rejectionsAnderson,
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50The importance of mentorsDonald B
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D.B. Rubin 607earlier). Wheeler was
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D.B. Rubin 609program at Harvard, s
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D.B. Rubin 61150.6 Interim time in
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D.B. Rubin 613my colleagues and stu
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616 Never ask for or give advicesum
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618 Never ask for or give adviceyou
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52Thirteen rulesBradley EfronDepart
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