2DkcTXceO

More documents

Recommendations

Info

L. Billard 32965931082471FIGURE 29.3Polythetic divisive tree based on histograms.FIGURE 29.4Simulated data — How many clusters?
330 The present’s futuremethod, to the classical observations (or, to classical surrogates such as themeans), identifies only two clusters, viz., C 1 = {Y 1 } and C 2 = {Y 2 ,Y 3 ,Y 4 ,Y 5 }.That is, since information such as the internal variations is not part of aclassical analysis, the classical clustering analyses are unable to identify observationsY 2 and Y 3 as being different from observations Y 4 and Y 5 .Example 29.3 Consider the data of Table 29.2 where for simplicity, we restrictattention to minimum and maximum monthly temperatures for fourmonths only, January, April, July, and October, Y 1 − Y 4 ,respectively,in1988for n = 10 weather stations. The interval values for station u =1,...,10and variable j =1,...,4 are denoted by Y uj =[a uj ,b uj ]. Elevation is alsoincluded as Y 5 ;noteY 5 is a classical value and so is a special case of aninterval value with a u5 ≡ [a u5 ,a u5 ]. The data are extracted from http://dss.ucar.edu/datasets/ds578.5 which contain annual monthly weathervalues for several variables for many stations in China over many years.TABLE 29.2Temperature intervals and elevation.Station January April July October Elevationu [a u1 ,b u1 ] [a u2 ,b u2 ] [a u3 ,b u3 ] [a u4 ,b u4 ] a u51 [−18.4, −7.5] [−0.1, 13.2] [17.0, 26.5] [0.6, 13.1] 4.822 [−20.0, −9.6] [0.2, 11.9] [17.8, 27.2] [−0.2, 12.5] 3.443 [−23.4, −15.5] [−4.5, 9.5] [12.9, 23.0] [−4.0, 8.9] 14.784 [−27.9, −16.0] [−1.5, 12.0] [16.1, 25.0] [−2.6, 10.9] 4.845 [−8.4, 9.0] [1.7, 16.4] [10.8, 23.2] [1.4, 18.7] 73.166 [2.3, 16.9] [9.9, 24.3] [17.4, 22.8] [14.5, 23.5] 32.967 [2.8, 16.6] [10.4, 23.4] [16.9, 24.4] [12.4, 19.7] 37.828 [10.0, 17.7] [15.8, 23.9] [24.2, 33.8] [19.2, 27.6] 2.389 [11.5, 17.7] [17.8, 24.2] [25.8, 33.5] [20.3, 26.9] 1.4410 [11.8, 19.2] [16.4, 22.7] [25.6, 32.6] [20.4, 27.3] 0.02A principal component analysis on these interval-valued data producesthe projections onto the PC 1 × PC 2 space shown in Figure 29.5. Detailsof the methodology and the visualization construction can be found in Le-Rademacher and Billard (2012), and further details of this particular data setare in Billard and Le-Rademacher (2012).Notice in particular that since the original observations are hypercubesin R p space, so we observe that the corresponding principal components arehypercubes in PC-space. The relative sizes of these PC hypercubes reflect therelative sizes of the data hypercubes. For example, if we compare the observedvalues of stations u =5andu = 10 in Table 29.2, it is clear that the intervalsacross the variables for u = 5 are on balance wider than are those for
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 and 348: 326 The present’s futureTABLE 29.
Page 349: 328 The present’s future124356981
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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?