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Institute of Mathematical Statistic
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Institute of Mathematical Statistic
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iv Contents Copulas and Decoupling
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vi Finally, thanks go the Statistic
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SCIENTIFIC PROGRAM The Second Erich
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x Recent Advances in Longitudinal D
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The Second Lehmann Symposium—Opti
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xiv Richard Davis Colorado State Un
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xvi Matthias Matheas Rice Universit
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xviii Hui Zhao University of Texas
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Proof. The maximum LR test rejects
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4. Location-scale families On likel
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6. Conclusions On likelihood ratio
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Student’s t-test for scale mixtur
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Student’s t-test for scale mixtur
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Student’s t-test for scale mixtur
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Optimality in multiple testing 17 w
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Optimality in multiple testing 19 I
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power 0.02 0.03 0.04 0.05 0.06 0.07
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Optimality in multiple testing 23 i
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Optimality in multiple testing 25 (
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Optimality in multiple testing 27 M
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Semiparametric transformation model
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2.1. The model Semiparametric trans
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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6.3. Part (ii) Put (6.1) (6.2) ˙Γ
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Semiparametric transformation model
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8. Proof of Proposition 2.3 Semipar
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Semiparametric transformation model
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Semiparametric transformation model
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Semiparametric transformation model
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Bayesian transformation hazard mode
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Bayesian transformation hazard mode
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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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Bayesian transformation hazard mode
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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Copulas, information, dependence an
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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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On Competing risk and degradation p
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On Competing risk and degradation p
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On Competing risk and degradation p
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On Competing risk and degradation p
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Restricted estimation in competing
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Restricted estimation in competing
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Restricted estimation in competing
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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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Robust tests for genetic associatio
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Robust tests for genetic associatio
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Robust tests for genetic associatio
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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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Linear prediction after model selec
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Linear prediction after model selec
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Linear prediction after model selec
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Linear prediction after model selec
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Linear prediction after model selec
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Linear prediction after model selec
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Appendix B: Proofs for Section 5 Li
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Linear prediction after model selec
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Local asymptotic minimax risk/asymm
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Local asymptotic minimax risk/asymm
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Then (3.5) Local asymptotic minimax
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Local asymptotic minimax risk/asymm
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Local asymptotic minimax risk/asymm
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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