7cMKg2

Recommendations

Info

20 “The whole women thing” 217 Nancy M. Reid 20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 20.2 “How many women are there in your department?” . . . . . 218 20.3 “Should I ask for more money?” . . . . . . . . . . . . . . . . 220 20.4 “I’m honored” . . . . . . . . . . . . . . . . . . . . . . . . . . 221 20.5 “I loved that photo” . . . . . . . . . . . . . . . . . . . . . . . 224 20.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 21 Reflections on diversity 229 Louise M. Ryan 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 21.2 Initiatives for minority students . . . . . . . . . . . . . . . . 230 21.3 Impact of the diversity programs . . . . . . . . . . . . . . . . 231 21.4 Gender issues . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 IV Reflections on the discipline 235 22 Why does statistics have two theories? 237 Donald A.S. Fraser 22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 22.2 65 years and what’s new . . . . . . . . . . . . . . . . . . . . 239 22.3 Where do the probabilities come from? . . . . . . . . . . . . 240 22.4 Inference for regular models: Frequency . . . . . . . . . . . . 243 22.5 Inference for regular models: Bootstrap . . . . . . . . . . . . 245 22.6 Inference for regular models: Bayes . . . . . . . . . . . . . . 246 22.7 The frequency-Bayes contradiction . . . . . . . . . . . . . . . 247 22.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 23 Conditioning is the issue 253 James O. Berger 23.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 23.2 Cox example and a pedagogical example . . . . . . . . . . . 254 23.3 Likelihood and stopping rule principles . . . . . . . . . . . . 255 23.4 What it means to be a frequentist . . . . . . . . . . . . . . . 257 23.5 Conditional frequentist inference . . . . . . . . . . . . . . . . 259 23.6 Final comments . . . . . . . . . . . . . . . . . . . . . . . . . 264 24 Statistical inference from a Dempster–Shafer perspective 267 Arthur P. Dempster 24.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 24.2 Personal probability . . . . . . . . . . . . . . . . . . . . . . . 268 24.3 Personal probabilities of “don’t know” . . . . . . . . . . . . . 269 24.4 The standard DS protocol . . . . . . . . . . . . . . . . . . . 271 24.5 Nonparametric inference . . . . . . . . . . . . . . . . . . . . 275 ix
x 24.6 Open areas for research . . . . . . . . . . . . . . . . . . . . . 276 25 Nonparametric Bayes 281 David B. Dunson 25.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 25.2 A brief history of NP Bayes . . . . . . . . . . . . . . . . . . . 284 25.3 Gazing into the future . . . . . . . . . . . . . . . . . . . . . . 287 26 How do we choose our default methods? 293 Andrew Gelman 26.1 Statistics: The science of defaults . . . . . . . . . . . . . . . 293 26.2 Ways of knowing . . . . . . . . . . . . . . . . . . . . . . . . . 295 26.3 The pluralist’s dilemma . . . . . . . . . . . . . . . . . . . . . 297 26.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 27 Serial correlation and Durbin–Watson bounds 303 T.W. Anderson 27.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 27.2 Circular serial correlation . . . . . . . . . . . . . . . . . . . . 304 27.3 Periodic trends . . . . . . . . . . . . . . . . . . . . . . . . . 305 27.4 Uniformly most powerful tests . . . . . . . . . . . . . . . . . 305 27.5 Durbin–Watson . . . . . . . . . . . . . . . . . . . . . . . . . 306 28 A non-asymptotic walk in probability and statistics 309 Pascal Massart 28.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 28.2 Model selection . . . . . . . . . . . . . . . . . . . . . . . . . 310 28.3 Welcome to Talagrand’s wonderland . . . . . . . . . . . . . . 315 28.4 Beyond Talagrand’s inequality . . . . . . . . . . . . . . . . . 318 29 The past’s future is now: What will the present’s future bring? 323 Lynne Billard 29.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 29.2 Symbolic data . . . . . . . . . . . . . . . . . . . . . . . . . . 324 29.3 Illustrations . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 29.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 30 Lessons in biostatistics 335 Norman E. Breslow 30.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 30.2 It’s the science that counts . . . . . . . . . . . . . . . . . . . 336 30.3 Immortal time . . . . . . . . . . . . . . . . . . . . . . . . . . 338 30.4 Multiplicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 30.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
Page 2 and 3: Contents Preface xvii Contributors
Page 4 and 5: 9.3 Optimal experimental design . .
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 20: Contributors xxiii Terry Speed Univ
Page 24 and 25: 1 AbriefhistoryoftheCommitteeof Pre
Page 26 and 27: I. Olkin 5 2. The Joint Committee o
Page 28 and 29: I. Olkin 7 1967, p. 48; April 1968,
Page 30 and 31: I. Olkin 9 1.3.1 The Visiting Lectu
Page 32 and 33: I. Olkin 11 place on Wednesdays of
Page 34 and 35: I. Olkin 13 1.4.2 R.A. Fisher Lectu
Page 36 and 37: I. Olkin 15 TABLE 1.4 Recipients of
Page 38 and 39: I. Olkin 17 TABLE 1.5 Recipients of
Page 40 and 41: I. Olkin 19 1.4.4 Elizabeth L. Scot
Page 42: Part II Reminiscences and personal
Page 45 and 46: 24 Reminiscences from Columbia the
Page 47 and 48: 26 Reminiscences from Columbia cess
Page 49 and 50: 28 Reminiscences from Columbia Refe
Page 51 and 52: 30 A career in statistics problem,
Page 53 and 54: 32 A career in statistics Laborator
Page 55 and 56: 34 A career in statistics Bruno de
Page 57 and 58:
36 A career in statistics At Stanfo
Page 59 and 60:
38 A career in statistics A trip to
Page 61 and 62:
40 A career in statistics Chernoff,
Page 63 and 64:
42 Wonderful field of statistics 4.
Page 65 and 66:
44 Wonderful field of statistics th
Page 67 and 68:
46 Wonderful field of statistics No
Page 70 and 71:
5 An unorthodox journey to statisti
Page 72 and 73:
J.P. Shaffer 51 with enough full-ti
Page 74 and 75:
J.P. Shaffer 53 piece of bad advice
Page 76 and 77:
J.P. Shaffer 55 Asimpleexampleisthe
Page 78:
J.P. Shaffer 57 Robinson, D. (2005)
Page 81 and 82:
60 Statistics before and after COPS
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 94 and 95:
7 The accidental biostatistics prof
Page 96 and 97:
D.J. Brogan 75 my prepared exam and
Page 98 and 99:
D.J. Brogan 77 7.5 Master’s degre
Page 100 and 101:
D.J. Brogan 79 7.9 Job offers — f
Page 102 and 103:
D.J. Brogan 81 I would be analyzing
Page 104 and 105:
8 Developing a passion for statisti
Page 106 and 107:
B.G. Lindsay 85 8.2 The first stati
Page 108 and 109:
B.G. Lindsay 87 ture. In the so-cal
Page 110 and 111:
B.G. Lindsay 89 My first PhD invest
Page 112 and 113:
B.G. Lindsay 91 he would humor me.
Page 114 and 115:
B.G. Lindsay 93 I nor my advisors k
Page 116 and 117:
B.G. Lindsay 95 References Bahadur,
Page 118 and 119:
9 Reflections on a statistical care
Page 120 and 121:
R.D. Cook 99 recommendation to the
Page 122 and 123:
R.D. Cook 101 no one sought my acqu
Page 124 and 125:
R.D. Cook 103 that is, Y X|P S X?Su
Page 126 and 127:
R.D. Cook 105 variance reduction de
Page 128 and 129:
R.D. Cook 107 in an application can
Page 130 and 131:
10 Science mixes it up with statist
Page 132 and 133:
K. Roeder 111 10.3 Some collaborati
Page 134 and 135:
K. Roeder 113 lations varied strong
Page 136:
K. Roeder 115 Since that year (1997
Page 139 and 140:
118 Lessons from a twisted career p
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 150 and 151:
12 Promoting equity Mary W. Gray De
Page 152 and 153:
M.W. Gray 131 the kit and its refin
Page 154 and 155:
M.W. Gray 133 of law and it was eas
Page 156 and 157:
M.W. Gray 135 the mathematical scie
Page 158:
M.W. Gray 137 References Affordable
Page 162 and 163:
13 Statistics in service to the nat
Page 164 and 165:
S.E. Fienberg 143 13.2 The National
Page 166 and 167:
S.E. Fienberg 145 between the acade
Page 168 and 169:
S.E. Fienberg 147 that have now bee
Page 170 and 171:
S.E. Fienberg 149 computed the area
Page 172 and 173:
S.E. Fienberg 151 Fienberg, S.E. (1
Page 174 and 175:
14 Where are the majors? Iain M. Jo
Page 176 and 177:
I.M. Johnstone 155 The NCES data sh
Page 178 and 179:
15 We live in exciting times Peter
Page 180 and 181:
P.G. Hall 159 15.2 Living with chan
Page 182 and 183:
P.G. Hall 161 Some of this work is
Page 184 and 185:
P.G. Hall 163 had some connection t
Page 186 and 187:
P.G. Hall 165 a nonparametric funct
Page 188 and 189:
P.G. Hall 167 References Abramowitz
Page 190:
P.G. Hall 169 Pitman, E.J.G. (1938)
Page 193 and 194:
172 Bright future of applied statis
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
178 The road travelled also to help
Page 201 and 202:
180 The road travelled gene-environ
Page 203 and 204:
182 The road travelled given marker
Page 205 and 206:
184 The road travelled future whole
Page 207 and 208:
186 The road travelled Jugurnauth-L
Page 210 and 211:
18 Ajourneyintostatisticalgeneticsa
Page 212 and 213:
X. Lin 191 Ilearnedwhilemakingmyjou
Page 214 and 215:
X. Lin 193 these simple methods in
Page 216 and 217:
X. Lin 195 0’s. The association b
Page 218 and 219:
X. Lin 197 expression, respectively
Page 220 and 221:
X. Lin 199 skills through seminars,
Page 222:
X. Lin 201 Pearl, J. (2001). Direct
Page 225 and 226:
204 Women in statistics in Canada m
Page 227 and 228:
206 Women in statistics in Canada I
Page 229 and 230:
208 Women in statistics in Canada A
Page 231 and 232:
210 Women in statistics in Canada T
Page 233 and 234:
212 Women in statistics in Canada A
Page 235 and 236:
214 Women in statistics in Canada R
Page 238 and 239:
20 “The whole women thing” Nanc
Page 240 and 241:
N.M. Reid 219 et al., 2012a), and t
Page 242 and 243:
N.M. Reid 221 2% to all female facu
Page 244 and 245:
N.M. Reid 223 Aparalleltothiscouldb
Page 246 and 247:
N.M. Reid 225 Happily for most read
Page 248 and 249:
N.M. Reid 227 Billard, L. and Ferbe
Page 250 and 251:
21 Reflections on diversity Louise
Page 252 and 253:
L.M. Ryan 231 would never have even
Page 254 and 255:
L.M. Ryan 233 21.4 Gender issues I
Page 256:
Part IV Reflections on the discipli
Page 259 and 260:
238 Statistics’ two theories Viox
Page 261 and 262:
240 Statistics’ two theories cont
Page 263 and 264:
242 Statistics’ two theories wher
Page 265 and 266:
244 Statistics’ two theories all
Page 267 and 268:
246 Statistics’ two theories a ca
Page 269 and 270:
248 Statistics’ two theories Freq
Page 271 and 272:
250 Statistics’ two theories Ackn
Page 273 and 274:
252 Statistics’ two theories Mars
Page 275 and 276:
254 Conditioning is the issue The p
Page 277 and 278:
256 Conditioning is the issue which
Page 279 and 280:
258 Conditioning is the issue athou
Page 281 and 282:
260 Conditioning is the issue Haar
Page 283 and 284:
262 Conditioning is the issue TABLE
Page 285 and 286:
264 Conditioning is the issue test
Page 287 and 288:
266 Conditioning is the issue Birnb
Page 289 and 290:
268 DS perspective on statistical i
Page 291 and 292:
Page 293 and 294:
Page 295 and 296:
Page 297 and 298:
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
282 Nonparametric Bayes the meaning
Page 305 and 306:
284 Nonparametric Bayes Prior (25.1
Page 307 and 308:
286 Nonparametric Bayes perhaps one
Page 309 and 310:
288 Nonparametric Bayes works well
Page 311 and 312:
290 Nonparametric Bayes of Statisti
Page 314 and 315:
26 How do we choose our default met
Page 316 and 317:
A. Gelman 295 and in the first cour
Page 318 and 319:
A. Gelman 297 distribution is a pow
Page 320 and 321:
A. Gelman 299 sampling) yet has bec
Page 322:
A. Gelman 301 Gelman, A. and Shaliz
Page 325 and 326:
304 Serial correlation and Durbin-W
Page 327 and 328:
Page 329 and 330:
Page 331 and 332:
310 Anon-asymptoticwalk ics, statis
Page 333 and 334:
312 Anon-asymptoticwalk γ(t, x) =
Page 335 and 336:
314 Anon-asymptoticwalk 28.2.3 Empi
Page 337 and 338:
316 Anon-asymptoticwalk to Cirel’
Page 339 and 340:
318 Anon-asymptoticwalk 28.4 Beyond
Page 341 and 342:
320 Anon-asymptoticwalk Boucheron,
Page 344 and 345:
29 The past’s future is now: What
Page 346 and 347:
L. Billard 325 bounce around, so th
Page 348 and 349:
L. Billard 327 TABLE 29.1 Airline d
Page 350 and 351:
L. Billard 329 6 5 9 3 10 8 2 4 7 1
Page 352 and 353:
L. Billard 331 FIGURE 29.5 PCA base
Page 354 and 355:
L. Billard 333 References Bickel, P
Page 356 and 357:
30 Lessons in biostatistics Norman
Page 358 and 359:
N.E. Breslow 337 most of its statis
Page 360 and 361:
N.E. Breslow 339 from complete remi
Page 362 and 363:
N.E. Breslow 341 transplant and tho
Page 364 and 365:
N.E. Breslow 343 TABLE 30.1 Regress
Page 366 and 367:
N.E. Breslow 345 30.5 Conclusion Th
Page 368:
N.E. Breslow 347 Partin, A.W., Yoo,
Page 371 and 372:
350 A vignette of discovery Early a
Page 373 and 374:
352 A vignette of discovery that in
Page 375 and 376:
354 A vignette of discovery observa
Page 377 and 378:
356 A vignette of discovery TABLE 3
Page 379 and 380:
358 A vignette of discovery ciate t
Page 381 and 382:
360 Statistics and public health re
Page 383 and 384:
Page 385 and 386:
Page 387 and 388:
Page 390 and 391:
33 Statistics in a new era for fina
Page 392 and 393:
T.L. Lai 371 (Rush et al., 2004) th
Page 394 and 395:
T.L. Lai 373 (2012). The monograph
Page 396 and 397:
T.L. Lai 375 loss function and assu
Page 398 and 399:
T.L. Lai 377 References Ait-Sahalia
Page 400:
T.L. Lai 379 Salmon, F. (2012). The
Page 403 and 404:
382 Meta-analyses and heterogeneity
Page 405 and 406:
Page 407 and 408:
Page 409 and 410:
Page 412 and 413:
35 Good health: Statistical challen
Page 414 and 415:
A.S. Whittemore 393 form a useful f
Page 416 and 417:
A.S. Whittemore 395 variate values.
Page 418 and 419:
A.S. Whittemore 397 the observed fl
Page 420 and 421:
A.S. Whittemore 399 represented, fo
Page 422 and 423:
A.S. Whittemore 401 Consider, for e
Page 424 and 425:
A.S. Whittemore 403 Venter, D.J., W
Page 426 and 427:
36 Buried treasures Michael A. Newt
Page 428 and 429:
M.A. Newton 407 F
Page 430 and 431:
M.A. Newton 409 When measured at se
Page 432:
M.A. Newton 411 Mason, D.M. and New
Page 435 and 436:
414 Survey sampling clustering. Ano
Page 437 and 438:
416 Survey sampling objective is th
Page 439 and 440:
418 Survey sampling Estimators ̂q
Page 441 and 442:
420 Survey sampling against model m
Page 443 and 444:
422 Survey sampling wrong, so can w
Page 445 and 446:
424 Survey sampling good design-bas
Page 447 and 448:
426 Survey sampling Box, G.E.P. (19
Page 449 and 450:
428 Survey sampling Rubin, D.B. (19
Page 451 and 452:
430 Environmental informatics Hasti
Page 453 and 454:
432 Environmental informatics this
Page 455 and 456:
434 Environmental informatics the d
Page 457 and 458:
436 Environmental informatics satel
Page 459 and 460:
438 Environmental informatics The m
Page 461 and 462:
440 Environmental informatics The c
Page 463 and 464:
442 Environmental informatics FIGUR
Page 465 and 466:
444 Environmental informatics 38.6
Page 467 and 468:
446 Environmental informatics error
Page 469 and 470:
448 Environmental informatics McLac
Page 472 and 473:
39 Ajourneywithstatisticalgenetics
Page 474 and 475:
E.A. Thompson 453 Population Meiosi
Page 476 and 477:
E.A. Thompson 455 TABLE 39.2 The ch
Page 478 and 479:
E.A. Thompson 457 39.5 The 2000s: A
Page 480 and 481:
E.A. Thompson 459 I have given many
Page 482 and 483:
E.A. Thompson 461 Fisher, R.A. (192
Page 484 and 485:
E.A. Thompson 463 Schena, M., Shalo
Page 486 and 487:
40 Targeted learning: From MLE to T
Page 488 and 489:
M. van der Laan 467 confidence inte
Page 490 and 491:
M. van der Laan 469 40.2.3 Target q
Page 492 and 493:
M. van der Laan 471 the doubly cens
Page 494 and 495:
M. van der Laan 473 40.4 Super lear
Page 496 and 497:
M. van der Laan 475 has the general
Page 498 and 499:
M. van der Laan 477 should also be
Page 500 and 501:
M. van der Laan 479 Petersen, M. an
Page 502 and 503:
41 Statistical model building, mach
Page 504 and 505:
G. Wahba 483 geometrical argument c
Page 506 and 507:
G. Wahba 485 matrix is a rank p pro
Page 508 and 509:
G. Wahba 487 as proposed by Vapnik
Page 510 and 511:
G. Wahba 489 details. In Corrada Br
Page 512 and 513:
G. Wahba 491 As is easy to see here
Page 514 and 515:
G. Wahba 493 ceedings of the Nation
Page 516:
G. Wahba 495 Vapnik, V. (1995). The
Page 519 and 520:
498 Sparsity and convexity . ^
Page 521 and 522:
500 Sparsity and convexity recover
Page 523 and 524:
502 Sparsity and convexity
Page 525 and 526:
504 Sparsity and convexity Referenc
Page 528 and 529:
43 Features of Big Data and sparses
Page 530 and 531:
J. Fan 509 chemotherapy is helpful
Page 532 and 533:
J. Fan 511 Realizations of two inde
Page 534 and 535:
J. Fan 513 selection of genes or SN
Page 536 and 537:
J. Fan 515 6 5 4 3 2 1 0 0
Page 538 and 539:
J. Fan 517 4 (a) m=2 10 (b) m=100 2
Page 540 and 541:
J. Fan 519 where · denotes the com
Page 542 and 543:
J. Fan 521 43.8 Conclusion Big Data
Page 544:
J. Fan 523 Hall, P., Titterington,
Page 547 and 548:
526 Rise of the machines ric regres
Page 549 and 550:
528 Rise of the machines ●●●
Page 551 and 552:
530 Rise of the machines 44.4.2 Cas
Page 553 and 554:
532 Rise of the machines is that th
Page 555 and 556:
534 Rise of the machines FIGURE 44.
Page 557 and 558:
536 Rise of the machines Acknowledg
Page 559 and 560:
538 NP-hard inference seem to appre
Page 561 and 562:
540 NP-hard inference signal or noi
Page 563 and 564:
542 NP-hard inference In the strict
Page 565 and 566:
544 NP-hard inference over equivale
Page 567 and 568:
546 NP-hard inference large to pass
Page 569 and 570:
548 NP-hard inference To illustrate
Page 571 and 572:
550 NP-hard inference model P Y (Y
Page 573 and 574:
552 NP-hard inference and accepted
Page 575 and 576:
554 NP-hard inference Here let us a
Page 577 and 578:
556 NP-hard inference The (middle)
Page 579 and 580:
558 NP-hard inference understanding
Page 581 and 582:
560 NP-hard inference Bouman, P., M
Page 583 and 584:
562 NP-hard inference Nason, G.P. (
Page 586 and 587:
46 Inspiration, aspiration, ambitio
Page 588 and 589:
C.F.J. Wu 567 works, he wrote of Pe
Page 590:
C.F.J. Wu 569 you will become, can
Page 593 and 594:
572 Personal reflections on the COP
Page 595 and 596:
Page 597 and 598:
Page 599 and 600:
Page 602 and 603:
48 Publishing without perishing and
Page 604 and 605:
M. Davidian 583 tion research. He w
Page 606 and 607:
M. Davidian 585 it accessible to pr
Page 608 and 609:
M. Davidian 587 emphasized the impo
Page 610 and 611:
M. Davidian 589 s/he needs to have
Page 612:
M. Davidian 591 enjoy what you do,
Page 615 and 616:
594 Converting rejections especiall
Page 617 and 618:
596 Converting rejections decent su
Page 619 and 620:
598 Converting rejections say, espe
Page 621 and 622:
600 Converting rejections final ana
Page 623 and 624:
602 Converting rejections Anderson,
Page 626 and 627:
50 The importance of mentors Donald
Page 628 and 629:
D.B. Rubin 607 earlier). Wheeler wa
Page 630 and 631:
D.B. Rubin 609 program at Harvard,
Page 632 and 633:
D.B. Rubin 611 50.6 Interim time in
Page 634:
D.B. Rubin 613 my colleagues and st
Page 637 and 638:
616 Never ask for or give advice su
Page 639 and 640:
618 Never ask for or give advice yo
Page 642 and 643:
52 Thirteen rules Bradley Efron Dep
show all

7cMKg2

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

Delete template?

Save as template?