- 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: xiv45.3 Multi-phase inference . . .
- Page 15 and 16: xviiiPast, Present, and Future of S
- Page 18 and 19: ContributorsTheodore W. AndersonSta
- Page 20: ContributorsxxiiiTerry SpeedUnivers
- 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,
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42 Wonderful field of statistics4.2
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44 Wonderful field of statisticsthi
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46 Wonderful field of statisticsNot
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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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62 Statistics before and after COPS
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64 Statistics before and after COPS
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66 Statistics before and after COPS
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68 Statistics before and after COPS
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70 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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120 Lessons from a twisted career p
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122 Lessons from a twisted career p
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124 Lessons from a twisted career p
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126 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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174 Bright future of applied statis
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176 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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270 DS perspective on statistical i
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272 DS perspective on statistical i
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274 DS perspective on statistical i
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276 DS perspective on statistical i
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278 DS perspective on statistical i
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280 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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306 Serial correlation and Durbin-W
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308 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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362 Statistics and public health re
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364 Statistics and public health re
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366 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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384 Meta-analyses and heterogeneity
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386 Meta-analyses and heterogeneity
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388 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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574 Personal reflections on the COP
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576 Personal reflections on the COP
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578 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