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R.A. Irizarry 17516.4 The bright futureI think that the data revolution is just getting started. Datasets are currentlybeing, or have already been, collected that contain, hidden in their complexity,important truths waiting to be discovered. These discoveries will increasethe scientific understanding of our world. Statisticians should be excited andready to play an important role in the new scientific renaissance driven by themeasurement revolution.ReferencesCrone, N.E., Hao, L., Hart, J., Boatman, D., Lesser, R.P., Irizarry, R., andGordon, B. (2001). Electrocorticographic gamma activity during wordproduction in spoken and sign language. Neurology, 57:2045–2053.DiPietro, J.A., Irizarry, R.A., Hawkins, M., Costigan, K.A., and Pressman,E.K. (2001). Cross-correlation of fetal cardiac and somatic activity as anindicator of antenatal neural development. American Journal of Obstetricsand Gynecology, 185:1421–1428.Gautier, L., Cope, L., Bolstad, B.M., and Irizarry, R.A. (2004). Affyanalysisof affymetrix genechip data at the probe level. Bioinformatics, 20:307–315.Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit,S., Ellis, B., Gautier, L., Ge, Y., Gentry, J. et al. (2004). Bioconductor:Open software development for computational biology andbioinformatics. Genome Biology, 5:R80.Irizarry, R.A. (2001). Local harmonic estimation in musical sound signals.Journal of the American Statistical Association, 96:357–367.Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J.,Scherf, U., and Speed, T.P. (2003). Exploration, normalization, and summariesof high density oligonucleotide array probe level data. Biostatistics,4:249–264.Irizarry, R.A., Ladd-Acosta, C., Wen, B., Wu, Z., Montano, C., Onyango, P.,Cui, H., Gabo, K., Rongione, M., Webster, M. et al. (2009). The humancolon cancer methylome shows similar hypo- and hypermethylation atconserved tissue-specific CpG island shores. Nature Genetics, 41:178–186.Irizarry, R.A., Tankersley, C., Frank, R., and Flanders, S. (2001). Assessinghomeostasis through circadian patterns. Biometrics, 57:1228–1237.
176 Bright future of applied statisticsvan’t Veer, L.J., Dai, H., Van De Vijver, M.J., He, Y.D., Hart, A.AM., Mao,M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T. etal. (2002). Gene expression profiling predicts clinical outcome of breastcancer. Nature, 415:530–536.
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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
Page 146 and 147: J.S. Rosenthal 125be open to whatev
Page 148: J.S. Rosenthal 12711.4 Final though
Page 151 and 152: 130 Promoting equityand fellowship
Page 153 and 154: 132 Promoting equityevaluate such e
Page 155 and 156: 134 Promoting equityfor equity. But
Page 157 and 158: 136 Promoting equityAssistant Secre
Page 160: Part IIIPerspectives on the fieldan
Page 163 and 164: 142 Statistics in service to the na
Page 175 and 176: 154 Where are the majors? ArtBachel
Page 177 and 178: 156 Where are the majors?References
Page 179 and 180: 158 Exciting timesCritically, Efron
Page 181 and 182: 160 Exciting timesIn general I find
Page 183 and 184: 162 Exciting timesI entered the Uni
Page 185 and 186: 164 Exciting times15.3.3 Joining th
Page 187 and 188: 166 Exciting timesvariability of a
Page 189 and 190: 168 Exciting timesHabemma, J.D.F.,
Page 192 and 193: 16The bright future of applied stat
Page 194 and 195: R.A. Irizarry 173FIGURE 16.1Illustr
Page 198 and 199: 17The road travelled: From statisti
Page 200 and 201: N. Chatterjee 179the Kaplan-Meyer e
Page 202 and 203: 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
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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
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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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