Optimality

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Invited Sessions New Investigators Javier Rojo, Organizer William C. Wojciechowski, Chair Gabriel Huerta Spatio-temporal analysis of Mexico city U of New Mexico ozone levels Sergio Juarez Robust and efficient estimation for U Veracruzana Mexico the generalized Pareto distribution William C. Wojciechowski Adaptive robust estimation by simulation Rice University Rudolf H. Riedi Optimal sampling strategies for tree-based Rice University time series Multiple hypothesis tests: New approaches—optimality issues Juliet P. Shaffer, Chair Juliet P. Shaffer Different types of optimality in multiple testing UC Berkeley Joseph Romano <strong>Optimality</strong> in stepwise hypothesis testing Stanford University Peter Westfall <strong>Optimality</strong> considerations in testing massive Texas Tech University numbers of hypotheses James R. Thompson, Chair Robustness Adrian Raftery Probabilistic weather forecasting using Bayesian U of Washington model averaging James R. Thompson The simugram: A robust measure of market risk Rice University Nozer D. Singpurwalla The hazard potential: An approach for specifying George Washington U models of survival Jef Teugels, Chair Extremes and Finance Richard A. Davis Regular variation and financial time series Colorado State University models Hansjoerg Albrecher Ruin theory in the presence of dependent claims University of Graz Austria Patrick L. Brockett A chance constrained programming approach to U of Texas, Austin pension plan management when asset returns are heavy tailed ix
x Recent Advances in Longitudinal Data Analysis Naisyin Wang, Chair Raymond J. Carroll Semiparametric efficiency in longitudinal marginal Texas A&M Univ. models Pushing Hsieh Some issues and results on nonparametric UC Davis maximum likelihood estimation in a joint model for survival and longitudinal data Jane-Ling Wang Functional regression and principal components UC Davis analysis for sparse longitudinal data Semiparametric and Nonparametric Testing David W. Scott, Chair Jeffrey D. Hart Semiparametric Bayesian and frequentist tests of Texas A&M Univ. trend for a large collection of variable stars Joseph Gastwirth Efficiency robust tests for linkage or association George Washington U. Irene Gijbels Nonparametric testing for monotonicity of U Catholique de Louvain a hazard rate Persi Diaconis, Chair Philosophy of Statistics David Freedman Some reflections on the foundations of statistics UC Berkeley Sir David Cox Some remarks on statistical inference Nuffield College, Oxford Deborah Mayo The theory of statistics as the “frequentist’s” theory Virginia Tech of inductive inference Shulamith T. Gross, Chair Special contributed session Victor Hugo de la Pena Pseudo maximization and self-normalized Columbia University processes Wei-Yin Loh Regression tree models for data from designed U of Wisconsin, Madison experiments Shulamith T. Gross Optimizing your chances of being funded by NSF and the NSF Baruch College/CUNY Contributed papers Aris Spanos, Virginia Tech: Where do statistical models come from? Revisiting the problem of specification Hannes Leeb, Yale University: The large-sample minimal coverage probability of confidence intervals in regression after model selection
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: SCIENTIFIC PROGRAM The Second Erich
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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≤ 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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On the false discovery proportion 4
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On the false discovery proportion 4
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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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