2DkcTXceO

More documents

Recommendations

Info

L. Billard 327TABLE 29.1Airline data (continued).Y 1 [270, 310) [310, 350) [350, 390) [390, 430) [430, 470) [470, 540]1 .07831 .07556 .02685 .00034 .00000 .000002 .00000 .00000 .00000 .00000 .00000 .000003 .00000 .00000 .00000 .00000 .00000 .000004 .03425 .02272 .00729 .00000 .00000 .000005 .00000 .00000 .00000 .00000 .00000 .000006 .00000 .00000 .00000 .00000 .00000 .000007 .34299 .21494 .08384 .01220 .00000 .000008 .00000 .00000 .00000 .00000 .00000 .000009 .00523 .00174 .00348 .00000 .00000 .0017410 .00000 .00000 .00000 .00000 .00000 .00000Y 2 [80, 100) [100, 120) [120, 140) [140, 160) [160, 200) [200, 240]1 .01773 .01411 .00637 .00654 .01532 .000002 .01403 .00281 .00281 .00000 .00281 .000003 .02094 .01440 .01276 .00884 .02716 .000004 .00797 .00661 .00356 .00051 .00220 .000005 .00865 .00576 .00576 .00576 .00865 .000006 .02883 .00835 .01366 .00835 .00531 .000007 .00762 .00305 .00152 .00762 .01372 .000008 .00817 .00635 .00227 .00136 .00318 .000009 .01916 .01394 .00871 .01220 .02091 .0000010 .00286 .00143 .00167 .00095 .00143 .00000Y 3 [105, 125) [125, 145) [145, 165) [165, 185) [185, 225) [225, 265]1 .00878 .00000 .00361 .00947 .00775 .000002 .00281 .00000 .00000 .00140 .00140 .000003 .01407 .00000 .01014 .01407 .01538 .000004 .00305 .00000 .00085 .00068 .00153 .000005 .00865 .00000 .00865 .00000 .00865 .000006 .01897 .00000 .00986 .00607 .00152 .000007 .00457 .00000 .00152 .00762 .00915 .000008 .00227 .00000 .00136 .00045 .00182 .000009 .01045 .00000 .01742 .01394 .00871 .0000010 .00095 .00000 .00072 .00048 .00024 .00000while airlines (1, 2) have comparable means, they differ from those for airline 4.That is, the classical surrogate analysis is based on the means only.ApolytheticdivisivetreebuiltontheEuclideanextendedIchino–Yaguchidistances for the histograms is shown in Figure 29.3; see Kim and Billard(2011) for this algorithm. The corresponding monothetic divisive tree is comparable.This tree is different again from those of Figures 29.1 and 29.2; thesedifferences reflect the fact that different clustering algorithms, along with differentdistance matrices and different methods, can construct quite differenttrees.
328 The present’s future12435698107FIGURE 29.1Ward’s method agglomerative tree based on histograms.Example 29.2 Figure 29.4 displays simulated individual classical observations(Y 1 ,Y 2 ) drawn from bivariate normal distributions N 2 (µ, Σ). Thereare five samples each with n = 100 observations. Sample S = 1 has meanµ = (5, 0), standard deviations σ 1 = σ 2 = .25 and correlation coefficientρ = 0; samples S=2,3 have µ =(1, 1), σ 1 = σ 2 = .25 and ρ = 0; and samplesS =4, 5haveµ =(1, 1), σ 1 = σ 2 =1andρ = .8. Each of the samples can beaggregated to produce a bivariate histogram observation Y s , s =1,...,5.When a divisive algorithm for histogram data is applied to these data, threeclusters emerge containing the observations C 1 = {Y 1 }, C 2 = {Y 2 ,Y 3 }, andC 3 = {Y 4 ,Y 5 }, respectively. In contrast, applying algorithms, e.g., a K-means12731049568FIGURE 29.2Ward’s method agglomerative tree based on means.
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:
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 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 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: 326 The present’s futureTABLE 29.
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?