Optimality

More documents

Recommendations

Info

Asheber Abebe Auburn University abebeas@auburn.edu Hansjoerg Albrecher Graz University albrecher@tugraz.at Demissie Alemayehu Pfizer alem@stat.columbia.edu E. Neely Atkinson University of Texas MD Anderson Cancer Center eatkinso@mdanderson.org Scott Baggett Rice University baggett@rice.edu Sarah Baraniuk University of Texas Houston School of Public Health sbaraniuk@sph.uth.tmc.edu Jose Luis Batun CIMAT batun@cimat.mx Debasis Bhattacharya Visva-Bharati, India Debases us@yahoo.com Chad Bhatti Rice University bhatticr@rice.edu Peter Bickel University of California, Berkeley bickel@stat.berkeley.edu Sharad Borle Rice University sborle@rice.edu Patrick Brockett University of Texas, Austin brockett@mail.utexas.edu Barry Brown University of Texas MD Anderson Cancer Center bwb@mdanderson.org Partial List of Participants xiii Ferry Butar Butar Sam Houston State University mth fbb@shsu.edu Raymond Carroll Texas A&M University carroll@stat.tamu.edu Wenyaw Chan University of Texas, Houston Health Science Center Wenyaw.Chan@uth.tmc.edu Jamie Chatman Rice University jchatman@rice.edu Cheng Cheng St Jude Hospital cheng.cheng@stjude.org Hyemi Choi Seoul National University hyemichoi@yahoo.com Blair Christian Rice University blairc@rice.edu Daren B. H. Cline Texas A&M University dcline@stat.tamu.edu Daniel Covarrubias Rice University dcorvarru@stat.rice.edu David R. Cox Nuffield College, Oxford david.cox@nut.ox.ac.uk Dennis Cox Rice University dcox@rice.edu Kalatu Davies Rice University kdavies@rice.edu Ginger Davis Rice University gmdavis@rice.edu
xiv Richard Davis Colorado State University rdavis@stat.colostate.edu Victor H. de la Peña Columbia University vp@stat.columbia.edu Li Deng Rice University lident@rice.edu Victor De Oliveira University of Arkansas vdo@uark.ed Persi Diaconis Stanford University Chris Ding Lawrence Berkeley Natl Lab chqding@lbl.gov Kjell Doksum University of Wisconsin doksum@stat.wisc.edu Joan Dong University of Texas MD Anderson Cancer Center qdong@mdanderson.org Wesley Eddings Kenyon College eddingsw@kenyon.edu Brad Efron Stanford University brad@statistics.stanford.edu Hammou El Barmi Baruch College hammou elbarmi@baruch.cuny.edu Kathy Ensor Rice University kathy@rice.edu Alan H. Feiveson Johnson Space Center alan.h.feiveson@nasa.gov Hector Flores Rice University hflores@rice.edu Garrett Fox Rice University gfox@stat.rice.edu David A. Freedman University of California, Berkeley freedman@stat.berkeley.edu Wenjiang Fu Texas A&M University wfu@stat.tamu.edu Joseph Gastwirth George Washington University jlgast@gwu.edu Susan Geller Texas A&M University geller@math.tamu.edu Musie Ghebremichael Rice University musie@rice.edu Irene Gijbels Catholic University of Louvin gijbels@stat.ucl.ac.be Nancy Glenn University of South Carolina nglenn@stat.sc.edu Carlos Gonzalez Universidad Veracruzana cglezand@tema.cum.mx Shulamith Gross NSF sgross@nsf.gov Xiangjun Gu University of Texas MD Anderson Cancer Center xgu@mdanderson.org Rudy Guerra Rice University rguerra@rice.edu Shu Han Rice University shuhan@rice.edu Robert Hardy University of Texas Health Science Center, Houston SPH bhardy@sph.uth.tmc.edu Jeffrey D. Hart Texas A&M University hart@stat.tamu.edu
Page 1 and 2: Institute of Mathematical Statistic
Page 3 and 4: Institute of Mathematical Statistic
Page 5 and 6: iv Contents Copulas and Decoupling
Page 7 and 8: vi Finally, thanks go the Statistic
Page 9 and 10: SCIENTIFIC PROGRAM The Second Erich
Page 11 and 12: x Recent Advances in Longitudinal D
Page 13: The Second Lehmann Symposium—Opti
Page 17 and 18: xvi Matthias Matheas Rice Universit
Page 19 and 20: xviii Hui Zhao University of Texas
Page 21 and 22: IMS Lecture Notes-Monograph Series
Page 23 and 24: Proof. The maximum LR test rejects
Page 25 and 26: 4. Location-scale families On likel
Page 27 and 28: 6. Conclusions On likelihood ratio
Page 31 and 32: Student’s t-test for scale mixtur
Page 37 and 38: Optimality in multiple testing 17 w
Page 39 and 40: Optimality in multiple testing 19 I
Page 41 and 42: power 0.02 0.03 0.04 0.05 0.06 0.07
Page 43 and 44: Optimality in multiple testing 23 i
Page 45 and 46: Optimality in multiple testing 25 (
Page 47 and 48: Optimality in multiple testing 27 M
Page 49 and 50: 6. Summary Optimality in multiple t
Page 51 and 52: Optimality in multiple testing 31 [
Page 55 and 56: On the false discovery proportion 3
Page 61 and 62: ≤ N� � N� P m=1 On the fals
Page 63 and 64: Since j≤ s, it follows that On th
Page 65 and 66:
0.025 0.02 0.015 0.01 0.005 On the
Page 67 and 68:
On the false discovery proportion 4
Page 69 and 70:
On the false discovery proportion 4
Page 71 and 72:
IMS Lecture Notes-Monograph Series
Page 73 and 74:
Massive multiple hypotheses testing
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
P value EDF qdf 0.0 0.2 0.4 0.6 0.8
Page 83 and 84:
Page 85 and 86:
Proof. See Appendix. Massive multip
Page 87 and 88:
X1 Massive multiple hypotheses test
Page 89 and 90:
Page 91 and 92:
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4
Page 93 and 94:
Then π0α ∗ cal π0α ∗ cal +
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Frequentist statistics: theory of i
Page 101 and 102:
Page 103 and 104:
2.3. Failure and confirmation Frequ
Page 105 and 106:
3.1. Types of null hypothesis Frequ
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
together with the probabilistic ass
Page 121 and 122:
Statistical models: problem of spec
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Modeling inequality, spread in mult
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Semiparametric transformation model
Page 155 and 156:
2.1. The model Semiparametric trans
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
6.3. Part (ii) Put (6.1) (6.2) ˙Γ
Page 181 and 182:
Page 183 and 184:
8. Proof of Proposition 2.3 Semipar
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Bayesian transformation hazard mode
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Cumulative Hazard 0.0 0.2 0.4 0.6 0
Page 199 and 200:
Hazard function 0.0 0.5 1.0 1.5 2.0
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Copulas, information, dependence an
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Regression trees 211 right model in
Page 233 and 234:
Abs(effects) 0.00 0.05 0.10 0.15 0.
Page 235 and 236:
Regression trees 215 of the tree. T
Page 237 and 238:
Regression trees 217 GUIDE methods
Page 239 and 240:
Regression trees 219 and E appear a
Page 241 and 242:
Regression trees 221 Table 3 Result
Page 243 and 244:
Solder =thick 2.5 Regression trees
Page 245 and 246:
Observed values 0 10 20 30 40 50 60
Page 247 and 248:
Regression trees 227 effects and in
Page 249 and 250:
Page 251 and 252:
On Competing risk and degradation p
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
Restricted estimation in competing
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
9. Conclusion 0.0 0.1 0.2 0.3 0.4 0
Page 273 and 274:
Page 275 and 276:
2.1. Maximin efficiency robust test
Page 277 and 278:
Robust tests for genetic associatio
Page 279 and 280:
Page 281 and 282:
Page 283 and 284:
Page 285 and 286:
Page 287 and 288:
1 . . . 1 i . . . . . . R j . . . O
Page 289 and 290:
Optimal sampling strategies 269 as
Page 291 and 292:
Optimal sampling strategies 271 γ1
Page 293 and 294:
Optimal sampling strategies 273 Def
Page 295 and 296:
Optimal sampling strategies 275 Usi
Page 297 and 298:
Optimal sampling strategies 277 Def
Page 299 and 300:
optimal 1 2 3 4 leaf sets 5 6 (leaf
Page 301 and 302:
6. Related work Optimal sampling st
Page 303 and 304:
Optimal sampling strategies 283 Cla
Page 305 and 306:
Optimal sampling strategies 285 Sin
Page 307 and 308:
Optimal sampling strategies 287 µ
Page 309 and 310:
Optimal sampling strategies 289 on
Page 311 and 312:
Page 313 and 314:
Linear prediction after model selec
Page 315 and 316:
y the P× 1 vector (3.1) ηn(p) = L
Page 317 and 318:
Page 319 and 320:
Page 321 and 322:
Page 323 and 324:
Page 325 and 326:
Page 327 and 328:
Page 329 and 330:
Appendix B: Proofs for Section 5 Li
Page 331 and 332:
Page 333 and 334:
Local asymptotic minimax risk/asymm
Page 335 and 336:
Page 337 and 338:
Then (3.5) Local asymptotic minimax
Page 339 and 340:
Page 341 and 342:
Page 343 and 344:
Moment-density estimation 323 may n
Page 345 and 346:
3. The bias and MSE of ˆf ∗ α M
Page 347 and 348:
Moment-density estimation 327 Corol
Page 349 and 350:
Moment-density estimation 329 Suppo
Page 351 and 352:
6. Simulations Moment-density estim
Page 353 and 354:
Moment-density estimation 333 [7] M
Page 355 and 356:
for positive α, and for α = 0 as
Page 357 and 358:
Asymptotics of the MDPDE 337 Lemma
Page 359:
Asymptotics of the MDPDE 339 asympt
show all

Optimality

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

Delete template?

Save as template?