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ContributorsxxiiiTerry SpeedUniversity of CaliforniaBerkeley, CAWalter and Eliza Hall Institute ofMedical ResearchMelbourne, AustraliaElizabeth A. ThompsonUniversity of WashingtonSeattle, WAMary E. ThompsonUniversity of WaterlooWaterloo, ONRobert J. TibshiraniStanford UniversityStanford, CAMark van der LaanUniversity of CaliforniaBerkeley, CAGrace WahbaUniversity of WisconsinMadison, WILarry A. WassermanCarnegie Mellon UniversityPittsburgh, PAAlice S. WhittemoreStanford UniversityStanford, CAC.F. Jeff WuGeorgia Institute of TechnologyAtlanta, GA
Page 2 and 3: ContentsPrefacexviiContributorsI Th
Page 5 and 6: viii15 We live in exciting times 15
Page 7 and 8: x24.6 Open areas for research . . .
Page 9 and 10: xii36 Buried treasures 405Michael A
Page 11 and 12: xiv45.3 Multi-phase inference . . .
Page 14 and 15: PrefaceStatistics is the science of
Page 16: PrefacexixPart V comprises seven ar
Page 19: xxiiNancy FlournoyUniversity of Mis
Page 24 and 25: 1AbriefhistoryoftheCommitteeofPresi
Page 26 and 27: I. Olkin 52. The Joint Committee on
Page 28 and 29: I. Olkin 71967, p. 48; April 1968,
Page 30 and 31: I. Olkin 91.3.1 The Visiting Lectur
Page 32 and 33: I. Olkin 11place on Wednesdays of t
Page 34 and 35: I. Olkin 131.4.2 R.A. Fisher Lectur
Page 36 and 37: I. Olkin 15TABLE 1.4Recipients of t
Page 38 and 39: I. Olkin 17TABLE 1.5Recipients of t
Page 40 and 41: I. Olkin 191.4.4 Elizabeth L. Scott
Page 42: Part IIReminiscences andpersonal re
Page 45 and 46: 24 Reminiscences from Columbiathe t
Page 47 and 48: 26 Reminiscences from Columbiacesse
Page 49 and 50: 28 Reminiscences from ColumbiaRefer
Page 51 and 52: 30 A career in statisticsproblem, b
Page 53 and 54: 32 A career in statisticsLaboratori
Page 55 and 56: 34 A career in statisticsBruno de F
Page 57 and 58: 36 A career in statisticsAt Stanfor
Page 59 and 60: 38 A career in statisticsA trip to
Page 61 and 62: 40 A career in statisticsChernoff,
Page 63 and 64: 42 Wonderful field of statistics4.2
Page 65 and 66: 44 Wonderful field of statisticsthi
Page 67 and 68: 46 Wonderful field of statisticsNot
Page 70 and 71:
5An unorthodox journey to statistic
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J.P. Shaffer 51with enough full-tim
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J.P. Shaffer 53piece of bad advice
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J.P. Shaffer 55Asimpleexampleisthed
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J.P. Shaffer 57Robinson, D. (2005).
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60 Statistics before and after COPS
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7The accidental biostatistics profe
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D.J. Brogan 75my prepared exam and
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D.J. Brogan 777.5 Master’s degree
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D.J. Brogan 797.9 Job offers — fi
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D.J. Brogan 81I would be analyzing
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8Developing a passion for statistic
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B.G. Lindsay 858.2 The first statis
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B.G. Lindsay 87ture. In the so-call
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B.G. Lindsay 89My first PhD investi
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B.G. Lindsay 91he would humor me. K
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B.G. Lindsay 93I nor my advisors kn
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B.G. Lindsay 95ReferencesBahadur, R
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9Reflections on a statistical caree
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R.D. Cook 99recommendation to the w
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R.D. Cook 101no one sought my acqui
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R.D. Cook 103that is, Y X|P S X?Sub
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R.D. Cook 105variance reduction des
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R.D. Cook 107in an application can
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10Science mixes it up with statisti
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K. Roeder 11110.3 Some collaborativ
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K. Roeder 113lations varied strongl
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K. Roeder 115Since that year (1997)
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118 Lessons from a twisted career p
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12Promoting equityMary W. GrayDepar
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M.W. Gray 131the kit and its refine
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M.W. Gray 133of law and it was easy
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M.W. Gray 135the mathematical scien
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M.W. Gray 137ReferencesAffordable H
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13Statistics in service to the nati
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S.E. Fienberg 14313.2 The National
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S.E. Fienberg 145between the academ
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S.E. Fienberg 147that have now been
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S.E. Fienberg 149computed the area
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S.E. Fienberg 151Fienberg, S.E. (19
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14Where are the majors?Iain M. John
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I.M. Johnstone 155The NCES data sho
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15We live in exciting timesPeter G.
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P.G. Hall 15915.2 Living with chang
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P.G. Hall 161Some of this work is h
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P.G. Hall 163had some connection to
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P.G. Hall 165a nonparametric functi
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P.G. Hall 167ReferencesAbramowitz,
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P.G. Hall 169Pitman, E.J.G. (1938).
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172 Bright future of applied statis
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178 The road travelledalso to help
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180 The road travelledgene-environm
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182 The road travelledgiven marker
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184 The road travelledfuture whole-
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186 The road travelledJugurnauth-Li
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18Ajourneyintostatisticalgeneticsan
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X. Lin 191Ilearnedwhilemakingmyjour
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X. Lin 193these simple methods in p
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X. Lin 1950’s. The association be
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X. Lin 197expression, respectively.
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X. Lin 199skills through seminars,
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X. Lin 201Pearl, J. (2001). Direct
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204 Women in statistics in Canadame
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206 Women in statistics in CanadaIn
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208 Women in statistics in CanadaA
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210 Women in statistics in CanadaTh
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212 Women in statistics in CanadaAl
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214 Women in statistics in CanadaRe
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20“The whole women thing”Nancy
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N.M. Reid 219et al., 2012a), and th
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N.M. Reid 2212% to all female facul
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N.M. Reid 223Aparalleltothiscouldbe
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N.M. Reid 225Happily for most reade
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N.M. Reid 227Billard, L. and Ferber
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21Reflections on diversityLouise M.
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L.M. Ryan 231would never have even
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L.M. Ryan 23321.4 Gender issuesI’
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Part IVReflections on thediscipline
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238 Statistics’ two theoriesVioxx
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240 Statistics’ two theoriescontr
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242 Statistics’ two theorieswhere
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244 Statistics’ two theoriesall w
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246 Statistics’ two theoriesa cas
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248 Statistics’ two theoriesFrequ
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250 Statistics’ two theoriesAckno
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252 Statistics’ two theoriesMarsh
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254 Conditioning is the issueThe pr
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256 Conditioning is the issuewhich
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258 Conditioning is the issueathoug
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260 Conditioning is the issueHaar p
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262 Conditioning is the issueTABLE
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264 Conditioning is the issuetest t
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266 Conditioning is the issueBirnba
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268 DS perspective on statistical i
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282 Nonparametric Bayesthe meaning
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284 Nonparametric BayesPrior (25.1)
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286 Nonparametric Bayesperhaps one
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288 Nonparametric Bayesworks well f
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290 Nonparametric Bayesof Statistic
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26How do we choose our default meth
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A. Gelman 295and in the first cours
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A. Gelman 297distribution is a powe
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A. Gelman 299sampling) yet has beco
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A. Gelman 301Gelman, A. and Shalizi
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304 Serial correlation and Durbin-W
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310 Anon-asymptoticwalkics, statist
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312 Anon-asymptoticwalkγ(t, x) =
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314 Anon-asymptoticwalk28.2.3 Empir
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316 Anon-asymptoticwalkto Cirel’s
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318 Anon-asymptoticwalk28.4 Beyond
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320 Anon-asymptoticwalkBoucheron, S
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29The past’s future is now: What
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L. Billard 325bounce around, so tha
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L. Billard 327TABLE 29.1Airline dat
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L. Billard 32965931082471FIGURE 29.
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L. Billard 331FIGURE 29.5PCA based
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L. Billard 333ReferencesBickel, P.J
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30Lessons in biostatisticsNorman E.
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N.E. Breslow 337most of its statist
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N.E. Breslow 339from complete remis
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N.E. Breslow 341transplant and thos
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N.E. Breslow 343TABLE 30.1Regressio
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N.E. Breslow 34530.5 ConclusionThe
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N.E. Breslow 347Partin, A.W., Yoo,
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350 A vignette of discoveryEarly at
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352 A vignette of discoverythat int
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354 A vignette of discoveryobservat
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356 A vignette of discoveryTABLE 31
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358 A vignette of discoveryciate th
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360 Statistics and public health re
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33Statistics in a new era for finan
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T.L. Lai 371(Rush et al., 2004) tha
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T.L. Lai 373(2012). The monograph b
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T.L. Lai 375loss function and assum
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T.L. Lai 377ReferencesAit-Sahalia,
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T.L. Lai 379Salmon, F. (2012). The
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382 Meta-analyses and heterogeneity
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35Good health: Statistical challeng
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A.S. Whittemore 393form a useful fo
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A.S. Whittemore 395variate values.
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A.S. Whittemore 397the observed fla
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A.S. Whittemore 399represented, for
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A.S. Whittemore 401Consider, for ex
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A.S. Whittemore 403Venter, D.J., We
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36Buried treasuresMichael A. Newton
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M.A. Newton 407FIGURE 36.1The relat
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M.A. Newton 409When measured at sev
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M.A. Newton 411Mason, D.M. and Newt
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414 Survey samplingclustering. Anot
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416 Survey samplingobjective is the
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418 Survey samplingEstimators ̂q a
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420 Survey samplingagainst model mi
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422 Survey samplingwrong, so can we
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424 Survey samplinggood design-base
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426 Survey samplingBox, G.E.P. (198
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428 Survey samplingRubin, D.B. (197
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430 Environmental informaticsHastie
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432 Environmental informaticsthis w
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434 Environmental informaticsthe da
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436 Environmental informaticssatell
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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
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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
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538 NP-hard inferenceseem to apprec
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540 NP-hard inferencesignal or nois
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542 NP-hard inferenceIn the stricte
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544 NP-hard inferenceover equivalen
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546 NP-hard inferencelarge to pass
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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 (
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554 NP-hard inferenceHere let us as
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556 NP-hard inferenceThe (middle) r
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558 NP-hard inferenceunderstanding
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560 NP-hard inferenceBouman, P., Me
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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
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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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