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Contributors xxiii Terry Speed University of California Berkeley, CA Walter and Eliza Hall Institute of Medical Research Melbourne, Australia Elizabeth A. Thompson University of Washington Seattle, WA Mary E. Thompson University of Waterloo Waterloo, ON Robert J. Tibshirani Stanford University Stanford, CA Mark van der Laan University of California Berkeley, CA Grace Wahba University of Wisconsin Madison, WI Larry A. Wasserman Carnegie Mellon University Pittsburgh, PA Alice S. Whittemore Stanford University Stanford, CA C.F. Jeff Wu Georgia Institute of Technology Atlanta, GA
Page 2 and 3: Contents Preface xvii Contributors
Page 4 and 5: 9.3 Optimal experimental design . .
Page 6 and 7: 20 “The whole women thing” 217
Page 8 and 9: 31 A vignette of discovery 349 Nanc
Page 10 and 11: xiii 41 Statistical model building,
Page 12: 50.3 Harvard University — the ear
Page 15 and 16: xviii Past, Present, and Future of
Page 18 and 19: Contributors Theodore W. Anderson S
Page 22: Part I The history of COPSS
Page 25 and 26: 4 A brief history of COPSS Onchiota
Page 27 and 28: 6 A brief history of COPSS The item
Page 29 and 30: 8 A brief history of COPSS TABLE 1.
Page 31 and 32: 10 A brief history of COPSS women o
Page 33 and 34: 12 A brief history of COPSS TABLE 1
Page 37 and 38: 16 A brief history of COPSS 1.4.3 G
Page 41 and 42: 20 A brief history of COPSS For mor
Page 44 and 45: 2 Reminiscences of the Columbia Uni
Page 46 and 47: I. Olkin 25 Bechhofer, Allan Birnba
Page 48 and 49: I. Olkin 27 Milton Sobel was the te
Page 50 and 51: 3 Acareerinstatistics Herman Cherno
Page 52 and 53: H. Chernoff 31 given three-day basi
Page 54 and 55: H. Chernoff 33 Savage tried to defe
Page 56 and 57: H. Chernoff 35 In working on an art
Page 58 and 59: H. Chernoff 37 measure the loss as
Page 60 and 61: H. Chernoff 39 References Albert, A
Page 62 and 63: 4 “. . . how wonderful the field
Page 64 and 65: D.R. Brillinger 43 He went on to be
Page 66 and 67: D.R. Brillinger 45 Tukey wrote many
Page 68: D.R. Brillinger 47 18. Personal com
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50 Unorthodox journey to statistics
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6 Statistics before and after my CO
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P.J. Bickel 61 6.3.1 Imperial Colle
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P.J. Bickel 63 make. This was first
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P.J. Bickel 65 We eventually develo
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P.J. Bickel 67 school interest, bio
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P.J. Bickel 69 References Bean, D.,
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P.J. Bickel 71 Meinshausen, N., Bic
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74 Accidental biostatistics profess
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84 Passion for statistics me is its
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86 Passion for statistics Their Bio
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88 Passion for statistics the first
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90 Passion for statistics In my exp
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92 Passion for statistics 8.5 Job a
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94 Passion for statistics period. A
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96 Passion for statistics Neyman, 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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11 Lessons from a twisted career pa
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J.S. Rosenthal 119 My math professo
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J.S. Rosenthal 121 contrary, it nul
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J.S. Rosenthal 123 of statistical r
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J.S. Rosenthal 125 be open to whate
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J.S. Rosenthal 127 11.4 Final thoug
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130 Promoting equity and fellowship
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132 Promoting equity evaluate such
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134 Promoting equity for equity. Bu
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136 Promoting equity Assistant Secr
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Part III Perspectives on the field
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142 Statistics in service to the na
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154 Where are the majors? Art Bach
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156 Where are the majors? Reference
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158 Exciting times Critically, Efro
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160 Exciting times In general I fin
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162 Exciting times I entered the Un
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164 Exciting times 15.3.3 Joining t
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166 Exciting times variability of a
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168 Exciting times Habemma, J.D.F.,
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16 The bright future of applied sta
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R.A. Irizarry 173 FIGURE 16.1 Illus
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R.A. Irizarry 175 16.4 The bright f
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17 The road travelled: From statist
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N. Chatterjee 179 the Kaplan-Meyer
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N. Chatterjee 181 17.4 Genome-wide
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N. Chatterjee 183 by any means. I j
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N. Chatterjee 185 right information
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N. Chatterjee 187 The risk of cance
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190 Journey into genetics and genom
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19 Reflections on women in statisti
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M.E. Thompson 205 Pearson in Englan
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M.E. Thompson 207 TABLE 19.1 The 14
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M.E. Thompson 209 time series, the
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M.E. Thompson 211 the founding Chai
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M.E. Thompson 213 19.7 The current
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M.E. Thompson 215 Yarmie, A. (2003)
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218 “The whole women thing” see
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220 “The whole women thing” of
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222 “The whole women thing” on
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224 “The whole women thing” 20.
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226 “The whole women thing” Ifi
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228 “The whole women thing” Lon
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230 Reflections on diversity as a s
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232 Reflections on diversity all of
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234 Reflections on diversity force
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22 Why does statistics have two the
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D.A.S. Fraser 239 22.2 65 years and
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D.A.S. Fraser 241 function is taken
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D.A.S. Fraser 243 22.4 Inference fo
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D.A.S. Fraser 245 that record the e
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D.A.S. Fraser 247 variable that giv
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D.A.S. Fraser 249 simple scalar cas
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D.A.S. Fraser 251 DiCiccio, T.J. an
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23 Conditioning is the issue James
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J.O. Berger 255 Pedagogical example
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J.O. Berger 257 The SRP does not sa
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J.O. Berger 259 23.5 Conditional fr
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J.O. Berger 261 conditional error p
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J.O. Berger 263 others). If x is th
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J.O. Berger 265 How does a frequent
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24 Statistical inference from a Dem
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A.P. Dempster 269 tion is simply un
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A.P. Dempster 271 As the world of s
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A.P. Dempster 273 when the X i are
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A.P. Dempster 275 24.5 Nonparametri
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A.P. Dempster 277 can easily happen
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A.P. Dempster 279 References Borel,
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25 Nonparametric Bayes David B. Dun
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D.B. Dunson 283 troduced, providing
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D.B. Dunson 285 els in spatial stat
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D.B. Dunson 287 methods as also pro
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D.B. Dunson 289 ing background, and
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D.B. Dunson 291 Murray, J.S., Dunso
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294 Choosing default methods imagin
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296 Choosing default methods (a) ma
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298 Choosing default methods partia
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300 Choosing default methods direct
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27 Serial correlation and Durbin-Wa
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T.W. Anderson 305 The characteristi
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T.W. Anderson 307 Consider the seri
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28 Anon-asymptoticwalkinprobability
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P. Massart 311 (at least for discre
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P. Massart 313 28.2.2 Non-asymptopi
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P. Massart 315 where P = sµ. In ot
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P. Massart 317 Hence by Talagrand
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P. Massart 319 References Akaike, H
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P. Massart 321 Talagrand, M. (1995)
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324 The present’s future data. In
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326 The present’s future TABLE 29
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328 The present’s future 1 2 4 3
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330 The present’s future method,
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332 The present’s future FIGURE 2
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334 The present’s future Ichino,
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336 Lessons in biostatistics 30.2 I
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338 Lessons in biostatistics progno
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340 Lessons in biostatistics time f
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342 Lessons in biostatistics meanin
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344 Lessons in biostatistics 75% to
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346 Lessons in biostatistics histol
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31 Avignetteofdiscovery Nancy Flour
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N. Flournoy 351 FIGURE 31.1 Inciden
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N. Flournoy 353 FIGURE 31.3 Mean re
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N. Flournoy 355 FIGURE 31.4 Kaplan-
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N. Flournoy 357 As the study procee
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32 Statistics and public health res
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R.L. Prentice 361 32.2 Public healt
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R.L. Prentice 363 from urinary excr
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R.L. Prentice 365 32.5 Clinical tri
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R.L. Prentice 367 Kaplan, E.L. and
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370 Statistics in a new era tive ef
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372 Statistics in a new era ular co
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374 Statistics in a new era joint d
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376 Statistics in a new era of the
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378 Statistics in a new era Lai, T.
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34 Meta-analyses: Heterogeneity can
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N.M. Laird 383 into the sum of a
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N.M. Laird 385 are “approximately
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N.M. Laird 387 included in the majo
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N.M. Laird 389 Yusuf, S., Peto, R.,
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392 Good health: Statistical challe
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406 Buried treasures measurement te
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408 Buried treasures In a most rewa
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410 Buried treasures lems different
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37 Survey sampling: Past controvers
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R.J. Little 415 37.2 Probability or
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R.J. Little 417 for the “design-b
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R.J. Little 419 estimate of varianc
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R.J. Little 421 Example 2 continued
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R.J. Little 423 37.3.4 The design-m
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R.J. Little 425 37.5 Conclusions I
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R.J. Little 427 Isaki, C.T. and Ful
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38 Environmental informatics: Uncer
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N. Cressie 431 and conquer” strat
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N. Cressie 433 In the last 20 years
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N. Cressie 435 Once the satellite h
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N. Cressie 437 FIGURE 38.1 Location
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N. Cressie 439 regional and seasona
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N. Cressie 441 ciated with the spat
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N. Cressie 443 is the set of all pi
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N. Cressie 445 challenge is to deve
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N. Cressie 447 Gourdji, S.M., Muell
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N. Cressie 449 Wikle, C.K., Milliff
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452 Statistical genetics TABLE 39.1
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454 Statistical genetics Stewart, 1
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456 Statistical genetics loci data
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458 Statistical genetics sufficient
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460 Statistical genetics Brown, M.D
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462 Statistical genetics Lange, K.
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464 Statistical genetics Thompson,
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466 Targeted learning ods for nonpa
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468 Targeted learning denote the ob
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470 Targeted learning Such an estim
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472 Targeted learning literature pr
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474 Targeted learning lihood estima
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476 Targeted learning 40.6 Some spe
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478 Targeted learning memory challe
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480 Targeted learning van der Laan,
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482 Ah-ha moment we have just seen.
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484 Ah-ha moment the data is unchan
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486 Ah-ha moment is well defined an
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488 Ah-ha moment it was something o
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490 Ah-ha moment defines a Euclidea
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492 Ah-ha moment participate in the
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494 Ah-ha moment Lee, Y., Wahba, G.
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42 In praise of sparsity and convex
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R.J. Tibshirani 499 TABLE 42.1 Asam
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R.J. Tibshirani 501 Cancer Epitheli
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R.J. Tibshirani 503 that the error
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R.J. Tibshirani 505 Tibshirani, R.J
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508 Features of Big Data problems e
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510 Features of Big Data learning s
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512 Features of Big Data 12 10 (a)
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514 Features of Big Data 2500 2000
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516 Features of Big Data As an illu
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518 Features of Big Data for some g
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520 Features of Big Data 43.7.3 Pro
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522 Features of Big Data Bühlmann,
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44 Rise of the machines Larry A. Wa
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L.A. Wasserman 527 if an idea requi
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L.A. Wasserman 529 ● ●● ●
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L.A. Wasserman 531 ● ● ● ●
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L.A. Wasserman 533 FIGURE 44.4 Simu
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L.A. Wasserman 535 44.7 Education a
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45 Atrioofinferenceproblemsthatcoul
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X.-L. Meng 539 it is directly roote
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X.-L. Meng 541 exactly, but this do
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X.-L. Meng 543 That is, when we do
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X.-L. Meng 545 (a) For what classes
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X.-L. Meng 547 subsequent analysts
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X.-L. Meng 549 can prove only that
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X.-L. Meng 551 In general, “What
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X.-L. Meng 553 vironmental progress
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X.-L. Meng 555 which will be compar
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X.-L. Meng 557 (a) Given partial kn
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X.-L. Meng 559 Acknowledgements The
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X.-L. Meng 561 Knuth, D. (1997). Th
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Part V Advice for the next generati
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566 Inspiration, aspiration, ambiti
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568 Inspiration, aspiration, ambiti
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47 Personal reflections on the COPS
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R.J. Carroll 573 them is, by defini
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R.J. Carroll 575 (Carroll, 2003). I
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R.J. Carroll 577 47.8 After the Pre
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R.J. Carroll 579 Carroll, R.J., Spi
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582 Publishing without perishing th
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584 Publishing without perishing be
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586 Publishing without perishing me
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588 Publishing without perishing al
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590 Publishing without perishing po
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49 Converting rejections into posit
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D.B. Rubin 595 This pair of submiss
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D.B. Rubin 597 There are several le
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D.B. Rubin 599 very sound article,
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D.B. Rubin 601 49.8 Conclusion Ihav
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D.B. Rubin 603 Rubin, D.B. (1972).
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606 Importance of mentors twice as
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608 Importance of mentors experimen
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610 Importance of mentors position
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612 Importance of mentors 50.7 The
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51 Never ask for or give advice, ma
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T. Speed 617 overly generous. Perha
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T. Speed 619 and for finding enjoym
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622 Thirteen rules 3. Waste a lot o
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