Optimality

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118 A. Spanos Neyman–Pearson philosophy of induction. The British Journal of the Philosophy of Science 57, 321–356. [15] McGuirk, A., J. Robertson and A. Spanos (1993). Modeling exchange rate dynamics: non-linear dependence and thick tails. Econometric Reviews 12, 33–63. [16] Mills, F. C. (1924). Statistical Methods. Henry Holt and Co., New York. [17] Neyman, J. (1950). First Course in Probability and Statistics, Henry Holt, New York. [18] Neyman, J. (1952). Lectures and Conferences on Mathematical Statistics and Probability, 2nd edition. U.S. Department of Agriculture, Washington. [19] Neyman, J. (1955). The problem of inductive inference. Communications on Pure and Applied Mathematics VIII, 13–46. [20] Neyman, J. (1957). Inductive behavior as a basic concept of philosophy of science. Revue Inst. Int. De Stat. 25, 7–22. [21] Neyman, J. (1969). Behavioristic points of view on mathematical statistics. In On Political Economy and Econometrics: Essays in Honour of Oskar Lange. Pergamon, Oxford, 445–462. [22] Neyman, J. (1971). Foundations of behavioristic statistics. In Foundations of Statistical Inference, Godambe, V. and Sprott, D., eds. Holt, Rinehart and Winston of Canada, Toronto, 1–13. [23] Neyman, J. (1976a). The emergence of mathematical statistics. In On the History of Statistics and Probability, Owen, D. B., ed. Dekker, New York, ch. 7. [24] Neyman, J. (1976b). A structural model of radiation effects in living cells. Proceedings of the National Academy of Sciences. 10, 3360–3363. [25] Neyman, J. (1977). Frequentist probability and frequentist statistics. Synthese 36, 97–131. [26] Neyman, J. and E. S. Pearson (1933). On the problem of the most efficient tests of statistical hypotheses. Phil. Trans. of the Royal Society A 231, 289– 337. [27] Pearson, K. (1895). Contributions to the mathematical theory of evolution II. Skew variation in homogeneous material. Philosophical Transactions of the Royal Society of London Series A 186, 343–414. [28] Pearson, K. (1920). The fundamental problem of practical statistics. Biometrika XIII, 1–16. [29] Rao, C. R. (1992). R. A. Fisher: The founder of modern statistics. Statistical Science 7, 34–48. [30] Rao, C. R. and Y. Wu (2001). On Model Selection. In P. Lahiri (2001), 1–64. [31] Spanos, A. (1986), Statistical Foundations of Econometric Modelling. Cambridge University Press, Cambridge. [32] Spanos, A. (1989). On re-reading Haavelmo: a retrospective view of econometric modeling. Econometric Theory. 5, 405–429. [33] Spanos, A. (1990). The simultaneous equations model revisited: statistical adequacy and identification. Journal of Econometrics 44, 87–108. [34] Spanos, A. (1994). On modeling heteroskedasticity: the Student’s t and elliptical regression models. Econometric Theory 10, 286–315. [35] Spanos, A. (1995). On theory testing in Econometrics: modeling with nonexperimental data. Journal of Econometrics 67, 189–226. [36] Spanos, A. (1999). Probability Theory and Statistical Inference: Econometric Modeling with Observational Data. Cambridge University Press, Cambridge.
Statistical models: problem of specification 119 [37] Spanos, A. (2000). Revisiting data mining: ‘hunting’ with or without a license. The Journal of Economic Methodology 7, 231–264. [38] Spanos, A. (2001). Time series and dynamic models. A Companion to Theoretical Econometrics, edited by B. Baltagi. Blackwell Publishers, Oxford, 585– 609, chapter 28. [39] Spanos, A. (2005). Structural vs. statistical models: Revisiting Kepler’s law of planetary motion. Working paper, Virginia Tech. [40] Spanos, A. (2006a). Econometrics in retrospect and prospect. In New Palgrave Handbook of Econometrics, vol. 1, Mills, T.C. and K. Patterson, eds. MacMillan, London. 3–58. [41] Spanos, A. (2006b). Revisiting the omitted variables argument: Substantive vs. statistical reliability of inference. Journal of Economic Methodology 13, 174–218. [42] Spanos, A. (2006c). The curve-fitting problem, Akaike-type model selection, and the error statistical approach. Working paper, Virginia Tech.
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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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IMS Lecture Notes-Monograph Series
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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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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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6. Summary Optimality in multiple t
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Optimality in multiple testing 31 [
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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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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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Semiparametric transformation model
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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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