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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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14 A brief history of COPSSTABLE 1.
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16 A brief history of COPSS1.4.3 Ge
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18 A brief history of COPSSTABLE 1.
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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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52 Unorthodox journey to statistics
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54 Unorthodox journey to statistics
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56 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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76 Accidental biostatistics profess
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78 Accidental biostatistics profess
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80 Accidental biostatistics profess
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82 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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102 Reflections on a statistical ca
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104 Reflections on a statistical ca
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106 Reflections on a statistical ca
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108 Reflections on a statistical ca
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110 Science mixes it up with statis
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112 Science mixes it up with statis
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114 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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144 Statistics in service to the na
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146 Statistics in service to the na
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148 Statistics in service to the na
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150 Statistics in service to the na
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152 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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192 Journey into genetics and genom
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194 Journey into genetics and genom
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196 Journey into genetics and genom
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198 Journey into genetics and genom
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200 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
- Page 272 and 273: D.A.S. Fraser 251DiCiccio, T.J. and
- Page 274 and 275: 23Conditioning is the issueJames O.
- Page 276 and 277: J.O. Berger 255Pedagogical example:
- Page 278 and 279: J.O. Berger 257The SRP does not say
- Page 280 and 281: J.O. Berger 25923.5 Conditional fre
- Page 282 and 283: J.O. Berger 261conditional error pr
- Page 284 and 285: J.O. Berger 263others). If x is the
- Page 286 and 287: J.O. Berger 265How does a frequenti
- Page 288 and 289: 24Statistical inference from aDemps
- Page 290 and 291: A.P. Dempster 269tion is simply une
- Page 292 and 293: A.P. Dempster 271As the world of st
- Page 294 and 295: A.P. Dempster 273when the X i are a
- Page 296 and 297: A.P. Dempster 27524.5 Nonparametric
- Page 298 and 299: A.P. Dempster 277can easily happen
- Page 300 and 301: A.P. Dempster 279ReferencesBorel,
- Page 302 and 303: 25Nonparametric BayesDavid B. Dunso
- Page 304 and 305: D.B. Dunson 283troduced, providing
- Page 306 and 307: D.B. Dunson 285els in spatial stati
- Page 308 and 309: D.B. Dunson 287methods as also prov
- Page 310 and 311: D.B. Dunson 289ing background, and
- Page 312: D.B. Dunson 291Murray, J.S., Dunson
- Page 315 and 316: 294 Choosing default methodsimagine
- Page 317 and 318: 296 Choosing default methods(a) mat
- Page 319 and 320: 298 Choosing default methodspartial
- Page 321: 300 Choosing default methodsdirecti
- Page 325 and 326: 304 Serial correlation and Durbin-W
- Page 327 and 328: 306 Serial correlation and Durbin-W
- Page 329 and 330: 308 Serial correlation and Durbin-W
- Page 331 and 332: 310 Anon-asymptoticwalkics, statist
- Page 333 and 334: 312 Anon-asymptoticwalkγ(t, x) =
- Page 335 and 336: 314 Anon-asymptoticwalk28.2.3 Empir
- Page 337 and 338: 316 Anon-asymptoticwalkto Cirel’s
- Page 339 and 340: 318 Anon-asymptoticwalk28.4 Beyond
- Page 341 and 342: 320 Anon-asymptoticwalkBoucheron, S
- Page 344 and 345: 29The past’s future is now: What
- Page 346 and 347: L. Billard 325bounce around, so tha
- Page 348 and 349: L. Billard 327TABLE 29.1Airline dat
- Page 350 and 351: L. Billard 32965931082471FIGURE 29.
- Page 352 and 353: L. Billard 331FIGURE 29.5PCA based
- Page 354 and 355: L. Billard 333ReferencesBickel, P.J
- Page 356 and 357: 30Lessons in biostatisticsNorman E.
- Page 358 and 359: N.E. Breslow 337most of its statist
- Page 360 and 361: N.E. Breslow 339from complete remis
- Page 362 and 363: N.E. Breslow 341transplant and thos
- Page 364 and 365: N.E. Breslow 343TABLE 30.1Regressio
- Page 366 and 367: N.E. Breslow 34530.5 ConclusionThe
- Page 368: N.E. Breslow 347Partin, A.W., Yoo,
- Page 371 and 372: 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
- 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
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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