Optimality

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Contributors to this volume Aaberge, R., Statistics Norway Baraniuk, R. G., Rice University Basu, A. K., Calcutta University Bhattacharya, D., Visva-Bharati University Bjerve, S., University of Oslo Cheng, C., St. Jude Children’s Research Hospital Cox, D. R., Nuffield College, Oxford Dabrowska, D. M., University of California, Los Angeles de la Peña, V. H., Columbia University Doksum, K., University of Wisconsin, Madison El Barmi, H., Baruch College, City University of New York Freidlin, B., National Cancer Institute Gastwirth, J. L., The George Washington University Ibragimov, R., Harvard University Ibrahim, J., University of North Carolina Juárez, S., Veracruzana University Leeb, H., Yale University Lehmann, E. L., University of California, Berkeley Loh, W.-Y., University of Wisconsin, Madison Mayo, D. G., Virginia Polytechnical Institute Mnatsakanov, R. M., West Virginia University Mukerjee, H., Wichita State University Ribeiro, V. J., Rice University Riedi, R. H., Rice University Romano, J. P., Stanford University Ruymgaart, F. H., Texas Tech University Schucany, W. R., Southern Methodist University Shaffer, J. P., University of California Shaikh, A. M., Stanford University Sharakhmetov, S., Tashkent State Economics University Singpurwalla, N. D., The George Washington University Spanos, A., Virginia Polytechnical Institute and State University Székely, G. J., Bowling Green State University, Hungarian Academy of Sciences Yin, G., MD Anderson Cancer Center Zheng, G., National Heart, Lung and Blood Institute vii
SCIENTIFIC PROGRAM The Second Erich L. Lehmann Symposium May 19–22, 2004 Rice University Symposium Chair and Organizer Javier Rojo Statistics Department, MS-138 Rice University 6100 Main Street Houston, TX 77005 Co-Chair Victor Perez-Abreu Probability and Statistics CIMAT Callejon Jalisco S/N Guanajuato, Mexico Plenary Speakers Erich L. Lehmann Conflicting principles in hypothesis testing UC Berkeley Peter Bickel From rank tests to semiparametrics UC Berkeley Ingram Olkin Probability models for survival and reliability analysis Stanford University D. R. Cox Graphical Markov models: A tool for interpretation Nuffield College Oxford Emanuel Parzen Data modeling, quantile/quartile functions, Texas A&M University confidence intervals, introductory statistics reform Bradley Efron Confidence regions and inferences for a multivariate Stanford University normal mean vector Kjell Doksum Modeling money UC Berkeley and UW Madison Persi Diaconis In praise of statistical theory Stanford University viii
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: vi Finally, thanks go the Statistic
Page 11 and 12: x Recent Advances in Longitudinal D
Page 13 and 14: The Second Lehmann Symposium—Opti
Page 15 and 16: xiv Richard Davis Colorado State Un
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
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On the false discovery proportion 3
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≤ N� � N� P m=1 On the fals
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Since j≤ s, it follows that On th
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0.025 0.02 0.015 0.01 0.005 On the
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IMS Lecture Notes-Monograph Series
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Massive multiple hypotheses testing
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P value EDF qdf 0.0 0.2 0.4 0.6 0.8
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Proof. See Appendix. Massive multip
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X1 Massive multiple hypotheses test
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0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4
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Then π0α ∗ cal π0α ∗ cal +
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Frequentist statistics: theory of i
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2.3. Failure and confirmation Frequ
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3.1. Types of null hypothesis Frequ
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together with the probabilistic ass
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Statistical models: problem of spec
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Modeling inequality, spread in mult
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Semiparametric transformation model
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2.1. The model Semiparametric trans
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6.3. Part (ii) Put (6.1) (6.2) ˙Γ
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8. Proof of Proposition 2.3 Semipar
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Bayesian transformation hazard mode
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Cumulative Hazard 0.0 0.2 0.4 0.6 0
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Hazard function 0.0 0.5 1.0 1.5 2.0
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Copulas, information, dependence an
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Regression trees 211 right model in
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Abs(effects) 0.00 0.05 0.10 0.15 0.
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Regression trees 215 of the tree. T
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Regression trees 217 GUIDE methods
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Regression trees 219 and E appear a
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Regression trees 221 Table 3 Result
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Solder =thick 2.5 Regression trees
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Observed values 0 10 20 30 40 50 60
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Regression trees 227 effects and in
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On Competing risk and degradation p
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Restricted estimation in competing
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9. Conclusion 0.0 0.1 0.2 0.3 0.4 0
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2.1. Maximin efficiency robust test
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Robust tests for genetic associatio
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1 . . . 1 i . . . . . . R j . . . O
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Optimal sampling strategies 269 as
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Optimal sampling strategies 271 γ1
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Optimal sampling strategies 273 Def
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Optimal sampling strategies 275 Usi
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Optimal sampling strategies 277 Def
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optimal 1 2 3 4 leaf sets 5 6 (leaf
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6. Related work Optimal sampling st
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Optimal sampling strategies 283 Cla
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Optimal sampling strategies 285 Sin
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Optimal sampling strategies 287 µ
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Optimal sampling strategies 289 on
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Linear prediction after model selec
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y the P× 1 vector (3.1) ηn(p) = L
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Appendix B: Proofs for Section 5 Li
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Local asymptotic minimax risk/asymm
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Then (3.5) Local asymptotic minimax
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Moment-density estimation 323 may n
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3. The bias and MSE of ˆf ∗ α M
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Moment-density estimation 327 Corol
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Moment-density estimation 329 Suppo
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6. Simulations Moment-density estim
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Moment-density estimation 333 [7] M
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for positive α, and for α = 0 as
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Asymptotics of the MDPDE 337 Lemma
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Asymptotics of the MDPDE 339 asympt
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Optimality

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