2DkcTXceO

More documents

Recommendations

Info

D.A.S. Fraser 251DiCiccio, T.J. and Young, G.A. (2008). Conditional properties of unconditionalparametric bootstrap procedures for inference in exponential families.Biometrika, 95:497–504.Fisher, R.A. (1930). Inverse probability. Proceedings of the Cambridge PhilosophicalSociety, 26:528–535.Fisher, R.A. (1935). The fiducial argument in statistical inference. Annals ofEugenics, 6:391–398.Fraser, A.M., Fraser, D.A.S., and Fraser, M.J. (2010a). Parameter curvaturerevisited and the Bayesian frequentist divergence. Statistical Research:Efron Volume, 44:335–346.Fraser, A.M., Fraser, D.A.S., and Staicu, A.M. (2010b). Second order ancillary:A differential view with continuity. Bernoulli, 16:1208–1223.Fraser, D.A.S. (1961a). The fiducial method and invariance. Biometrika,48:261–280.Fraser, D.A.S. (1961b). On fiducial inference. The Annals of MathematicalStatistics, 32:661–676.Fraser, D.A.S. (2011). Is Bayes posterior just quick and dirty confidence?(with discussion). Statistical Science, 26:299–316.Fraser, D.A.S. (2013). Can Bayes inference be second-order for scalar parameters?Submittedforpublication.Fraser, D.A.S. and Reid, N.M. (1993). Third order asymptotic models: Likelihoodfunctions leading to accurate approximations for distribution functions.Statistica Sinica, 3:67–82.Fraser, D.A.S. and Reid, N.M. (1995). Ancillaries and third order significance.Utilitas Mathematica, 47:33–53.Fraser, D.A.S. and Reid, N.M. (2013). Assessing a parameter of interest:Higher-order methods and the bootstrap. Submittedforpublication.Fraser, D.A.S. and Rousseau, J. (2008). Studentization and deriving accuratep-values. Biometrika, 95:1–16.Jeffreys, H. (1946). An invariant form for the prior probability in estimationproblems. Proceedings of the Royal Society, Series A, 186:453–461.Léger, C. (2013). The Statistical Society of Canada (SSC) response to theNSERC consultation on the evaluation of the Discovery Grants Program.SSC Liaison, 27(2):12–21.Lindley, D.V. (1958). Fiducial distribution and Bayes theorem. Journal ofthe Royal Statistical Society, Series B, 20:102–107.
252 Statistics’ two theoriesMarshall, M. (2012). Seismologists found guilty of manslaughter. New Scientist,October22,2012.Neyman, J. (1937). Outline of a theory of statistical estimation based on theclassical theory of probability. Philosophical Transactions of the RoyalSociety, Series A, 237:333–380.Pielke, R. (2011). Lessons of the L’Aquila lawsuit. Bridges 31, http://www.bbc.co.uk/news/world-europe-20025626.Prats, J. (2012). The L’Aquila earthquake. Significance, 9:13–16.Welch, B.L. and Peers, H.W. (1963). On formulae for confidence points basedon intervals of weighted likelihoods. Journal of the Royal Statistical Society,Series B, 25:318–329.
Page 2 and 3:
ContentsPrefacexviiContributorsI Th
Page 5 and 6:
viii15 We live in exciting times 15
Page 7 and 8:
x24.6 Open areas for research . . .
Page 9 and 10:
xii36 Buried treasures 405Michael A
Page 11 and 12:
xiv45.3 Multi-phase inference . . .
Page 14 and 15:
PrefaceStatistics is the science of
Page 16:
PrefacexixPart V comprises seven ar
Page 19 and 20:
xxiiNancy FlournoyUniversity of Mis
Page 22:
Part IThe history of COPSS
Page 25 and 26:
4 A brief history of COPSSOnchiota
Page 27 and 28:
6 A brief history of COPSSThe items
Page 29 and 30:
8 A brief history of COPSSTABLE 1.1
Page 31 and 32:
10 A brief history of COPSSwomen of
Page 33 and 34:
12 A brief history of COPSSTABLE 1.
Page 35 and 36:
Page 37 and 38:
16 A brief history of COPSS1.4.3 Ge
Page 39 and 40:
Page 41 and 42:
20 A brief history of COPSSFor more
Page 44 and 45:
2Reminiscences of the Columbia Univ
Page 46 and 47:
I. Olkin 25Bechhofer, Allan Birnbau
Page 48 and 49:
I. Olkin 27Milton Sobel was the tea
Page 50 and 51:
3AcareerinstatisticsHerman Chernoff
Page 52 and 53:
H. Chernoff 31given three-day basic
Page 54 and 55:
H. Chernoff 33Savage tried to defen
Page 56 and 57:
H. Chernoff 35In working on an arti
Page 58 and 59:
H. Chernoff 37measure the loss as t
Page 60 and 61:
H. Chernoff 39ReferencesAlbert, A.E
Page 62 and 63:
4“. . . how wonderful the field o
Page 64 and 65:
D.R. Brillinger 43He went on to be
Page 66 and 67:
D.R. Brillinger 45Tukey wrote many
Page 68:
D.R. Brillinger 4718. Personal comm
Page 71 and 72:
50 Unorthodox journey to statistics
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 80 and 81:
6Statistics before and after my COP
Page 82 and 83:
P.J. Bickel 616.3.1 Imperial Colleg
Page 84 and 85:
P.J. Bickel 63make. This was first
Page 86 and 87:
P.J. Bickel 65We eventually develop
Page 88 and 89:
P.J. Bickel 67school interest, biol
Page 90 and 91:
P.J. Bickel 69ReferencesBean, D., B
Page 92:
P.J. Bickel 71Meinshausen, N., Bick
Page 95 and 96:
74 Accidental biostatistics profess
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
84 Passion for statisticsme is its
Page 107 and 108:
86 Passion for statisticsTheir Biom
Page 109 and 110:
88 Passion for statisticsthe first
Page 111 and 112:
90 Passion for statisticsIn my expe
Page 113 and 114:
92 Passion for statistics8.5 Job an
Page 115 and 116:
94 Passion for statisticsperiod. An
Page 117 and 118:
96 Passion for statisticsNeyman, J.
Page 119 and 120:
98 Reflections on a statistical car
Page 121 and 122:
100 Reflections on a statistical ca
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
110 Science mixes it up with statis
Page 133 and 134:
Page 135 and 136:
Page 138 and 139:
11Lessons from a twisted career pat
Page 140 and 141:
J.S. Rosenthal 119My math professor
Page 142 and 143:
J.S. Rosenthal 121contrary, it null
Page 144 and 145:
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 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
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 196 and 197:
R.A. Irizarry 17516.4 The bright fu
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 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 221 and 222: 200 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
Page 247 and 248: 226 “The whole women thing”Ifin
Page 249 and 250: 228 “The whole women thing”Lond
Page 251 and 252: 230 Reflections on diversityas a st
Page 253 and 254: 232 Reflections on diversityall of
Page 255 and 256: 234 Reflections on diversityforce m
Page 258 and 259: 22Why does statistics have two theo
Page 260 and 261: D.A.S. Fraser 23922.2 65 years and
Page 262 and 263: D.A.S. Fraser 241function is taken
Page 264 and 265: D.A.S. Fraser 24322.4 Inference for
Page 266 and 267: D.A.S. Fraser 245that record the ef
Page 268 and 269: D.A.S. Fraser 247variable that give
Page 270 and 271: D.A.S. Fraser 249simple scalar case
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 and 322: 300 Choosing default methodsdirecti
Page 324 and 325:
27Serial correlation and Durbin-Wat
Page 326 and 327:
T.W. Anderson 305The characteristic
Page 328 and 329:
T.W. Anderson 307Consider the seria
Page 330 and 331:
28Anon-asymptoticwalkinprobabilitya
Page 332 and 333:
P. Massart 311(at least for discret
Page 334 and 335:
P. Massart 31328.2.2 Non-asymptopia
Page 336 and 337:
P. Massart 315where P = sµ. In oth
Page 338 and 339:
P. Massart 317Hence by Talagrand’
Page 340 and 341:
P. Massart 319ReferencesAkaike, H.
Page 342:
P. Massart 321Talagrand, M. (1995).
Page 345 and 346:
324 The present’s futuredata. In
Page 347 and 348:
326 The present’s futureTABLE 29.
Page 349 and 350:
328 The present’s future124356981
Page 351 and 352:
330 The present’s futuremethod, t
Page 353 and 354:
332 The present’s futureFIGURE 29
Page 355 and 356:
334 The present’s futureIchino, M
Page 357 and 358:
336 Lessons in biostatistics30.2 It
Page 359 and 360:
338 Lessons in biostatisticsprognos
Page 361 and 362:
340 Lessons in biostatisticstime fo
Page 363 and 364:
342 Lessons in biostatisticsmeaning
Page 365 and 366:
344 Lessons in biostatistics75% to
Page 367 and 368:
346 Lessons in biostatisticshistolo
Page 370 and 371:
31AvignetteofdiscoveryNancy Flourno
Page 372 and 373:
N. Flournoy 351FIGURE 31.1Incidence
Page 374 and 375:
N. Flournoy 353FIGURE 31.3Mean resp
Page 376 and 377:
N. Flournoy 355FIGURE 31.4Kaplan-Me
Page 378 and 379:
N. Flournoy 357As the study proceed
Page 380 and 381:
32Statistics and public health rese
Page 382 and 383:
R.L. Prentice 36132.2 Public health
Page 384 and 385:
R.L. Prentice 363from urinary excre
Page 386 and 387:
R.L. Prentice 36532.5 Clinical tria
Page 388:
R.L. Prentice 367Kaplan, E.L. and M
Page 391 and 392:
370 Statistics in a new erative eff
Page 393 and 394:
372 Statistics in a new eraular com
Page 395 and 396:
374 Statistics in a new erajoint de
Page 397 and 398:
376 Statistics in a new eraof the n
Page 399 and 400:
378 Statistics in a new eraLai, T.L
Page 402 and 403:
34Meta-analyses: Heterogeneity can
Page 404 and 405:
N.M. Laird 383into the sum of a “
Page 406 and 407:
N.M. Laird 385are “approximately
Page 408 and 409:
N.M. Laird 387included in the major
Page 410:
N.M. Laird 389Yusuf, S., Peto, R.,
Page 413 and 414:
392 Good health: Statistical challe
Page 415 and 416:
Page 417 and 418:
Page 419 and 420:
Page 421 and 422:
Page 423 and 424:
Page 425 and 426:
Page 427 and 428:
406 Buried treasuresmeasurement tec
Page 429 and 430:
408 Buried treasuresIn a most rewar
Page 431 and 432:
410 Buried treasureslems different
Page 434 and 435:
37Survey sampling: Past controversi
Page 436 and 437:
R.J. Little 41537.2 Probability or
Page 438 and 439:
R.J. Little 417for the “design-ba
Page 440 and 441:
R.J. Little 419estimate of variance
Page 442 and 443:
R.J. Little 421Example 2 continued
Page 444 and 445:
R.J. Little 42337.3.4 The design-mo
Page 446 and 447:
R.J. Little 42537.5 ConclusionsI am
Page 448 and 449:
R.J. Little 427Isaki, C.T. and Full
Page 450 and 451:
38Environmental informatics: Uncert
Page 452 and 453:
N. Cressie 431and conquer” strate
Page 454 and 455:
N. Cressie 433In the last 20 years,
Page 456 and 457:
N. Cressie 435Once the satellite ha
Page 458 and 459:
N. Cressie 437FIGURE 38.1Locations
Page 460 and 461:
N. Cressie 439regional and seasonal
Page 462 and 463:
N. Cressie 441ciated with the spati
Page 464 and 465:
N. Cressie 443is the set of all pix
Page 466 and 467:
N. Cressie 445challenge is to devel
Page 468 and 469:
N. Cressie 447Gourdji, S.M., Muelle
Page 470:
N. Cressie 449Wikle, C.K., Milliff,
Page 473 and 474:
452 Statistical geneticsTABLE 39.1T
Page 475 and 476:
454 Statistical geneticsStewart, 19
Page 477 and 478:
456 Statistical geneticslocidata
Page 479 and 480:
458 Statistical geneticssufficientl
Page 481 and 482:
460 Statistical geneticsBrown, M.D.
Page 483 and 484:
462 Statistical geneticsLange, K. a
Page 485 and 486:
464 Statistical geneticsThompson, E
Page 487 and 488:
466 Targeted learningods for nonpar
Page 489 and 490:
468 Targeted learningdenote the obs
Page 491 and 492:
470 Targeted learningSuch an estima
Page 493 and 494:
472 Targeted learningliterature pro
Page 495 and 496:
474 Targeted learninglihood estimat
Page 497 and 498:
476 Targeted learning40.6 Some spec
Page 499 and 500:
478 Targeted learningmemory challen
Page 501 and 502:
480 Targeted learningvan der Laan,
Page 503 and 504:
482 Ah-ha momentwe have just seen.
Page 505 and 506:
484 Ah-ha momentthe data is unchang
Page 507 and 508:
486 Ah-ha momentis well defined and
Page 509 and 510:
488 Ah-ha momentit was something of
Page 511 and 512:
490 Ah-ha momentdefines a Euclidean
Page 513 and 514:
492 Ah-ha momentparticipate in the
Page 515 and 516:
494 Ah-ha momentLee, Y., Wahba, G.,
Page 518 and 519:
42In praise of sparsity and convexi
Page 520 and 521:
R.J. Tibshirani 499TABLE 42.1Asampl
Page 522 and 523:
R.J. Tibshirani 501CancerEpithelial
Page 524 and 525:
R.J. Tibshirani 503that the error v
Page 526:
R.J. Tibshirani 505Tibshirani, R.J.
Page 529 and 530:
508 Features of Big Dataproblems en
Page 531 and 532:
510 Features of Big Datalearning st
Page 533 and 534:
512 Features of Big Data1210(a)p =
Page 535 and 536:
514 Features of Big Data25002000150
Page 537 and 538:
516 Features of Big DataAs an illus
Page 539 and 540:
518 Features of Big Datafor some gi
Page 541 and 542:
520 Features of Big Data43.7.3 Prop
Page 543 and 544:
522 Features of Big DataBühlmann,
Page 546 and 547:
44Rise of the machinesLarry A. Wass
Page 548 and 549:
L.A. Wasserman 527if an idea requir
Page 550 and 551:
L.A. Wasserman 529●●●● ●
Page 552 and 553:
L.A. Wasserman 531●●●●●
Page 554 and 555:
L.A. Wasserman 533FIGURE 44.4Simula
Page 556 and 557:
L.A. Wasserman 53544.7 Education an
Page 558 and 559:
45Atrioofinferenceproblemsthatcould
Page 560 and 561:
X.-L. Meng 539it is directly rooted
Page 562 and 563:
X.-L. Meng 541exactly, but this doe
Page 564 and 565:
X.-L. Meng 543That is, when we do n
Page 566 and 567:
X.-L. Meng 545(a) For what classes
Page 568 and 569:
X.-L. Meng 547subsequent analysts c
Page 570 and 571:
X.-L. Meng 549can prove only that (
Page 572 and 573:
X.-L. Meng 551In general, “What t
Page 574 and 575:
X.-L. Meng 553vironmental progress
Page 576 and 577:
X.-L. Meng 555which will be compare
Page 578 and 579:
X.-L. Meng 557(a) Given partial kno
Page 580 and 581:
X.-L. Meng 559AcknowledgementsThe m
Page 582 and 583:
X.-L. Meng 561Knuth, D. (1997). The
Page 584:
Part VAdvice for the nextgeneration
Page 587 and 588:
566 Inspiration, aspiration, ambiti
Page 589 and 590:
568 Inspiration, aspiration, ambiti
Page 592 and 593:
47Personal reflections on the COPSS
Page 594 and 595:
R.J. Carroll 573them is, by definit
Page 596 and 597:
R.J. Carroll 575(Carroll, 2003). In
Page 598 and 599:
R.J. Carroll 57747.8 After the Pres
Page 600:
R.J. Carroll 579Carroll, R.J., Spie
Page 603 and 604:
582 Publishing without perishingthe
Page 605 and 606:
584 Publishing without perishingbeg
Page 607 and 608:
586 Publishing without perishingme,
Page 609 and 610:
588 Publishing without perishingalr
Page 611 and 612:
590 Publishing without perishingpor
Page 614 and 615:
49Converting rejections into positi
Page 616 and 617:
D.B. Rubin 595This pair of submissi
Page 618 and 619:
D.B. Rubin 597There are several les
Page 620 and 621:
D.B. Rubin 599very sound article, w
Page 622 and 623:
D.B. Rubin 60149.8 ConclusionIhaveb
Page 624:
D.B. Rubin 603Rubin, D.B. (1972). A
Page 627 and 628:
606 Importance of mentorstwice as t
Page 629 and 630:
608 Importance of mentorsexperiment
Page 631 and 632:
610 Importance of mentorsposition a
Page 633 and 634:
612 Importance of mentors50.7 The t
Page 636 and 637:
51Never ask for or give advice, mak
Page 638 and 639:
T. Speed 617overly generous. Perhap
Page 640:
T. Speed 619and for finding enjoyme
Page 643:
622 Thirteen rules3. Waste a lot of
show all

2DkcTXceO

Create successful ePaper yourself

Delete template?

Save as template?