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50.3 Harvard University — the early years . . . . . . . . . . . . . 608 50.4 My years in statistics as a PhD student . . . . . . . . . . . . 609 50.5 The decade at ETS . . . . . . . . . . . . . . . . . . . . . . . 610 50.6 Interim time in DC at EPA, at the University of Wisconsin, and the University of Chicago . . . . . . . . . . . . . . . . . 611 50.7 The three decades at Harvard . . . . . . . . . . . . . . . . . 612 50.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 51 Never ask for or give advice, make mistakes, accept mediocrity, enthuse 615 Terry Speed 51.1 Never ask for or give advice . . . . . . . . . . . . . . . . . . . 615 51.2 Make mistakes . . . . . . . . . . . . . . . . . . . . . . . . . . 616 51.3 Accept mediocrity . . . . . . . . . . . . . . . . . . . . . . . . 617 51.4 Enthuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618 52 Thirteen rules 621 Bradley Efron 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 52.2 Thirteen rules for giving a really bad talk . . . . . . . . . . . 621 xv
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 14 and 15: Preface Statistics is the science o
Page 16: Preface xix Part V comprises seven
Page 19 and 20: xxii Nancy Flournoy University of M
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
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4 “. . . how wonderful the field
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D.R. Brillinger 43 He went on to be
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D.R. Brillinger 45 Tukey wrote many
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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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