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作者：Zhou, HH; Hwang, JTG
作者单位：Yale University; Cornell University
摘要：Many statistical practices involve choosing between a full model and reduced models where some coefficients are reduced to zero. Data were used to select a model with estimated coefficients. Is it possible to do so and still come up with an estimator always better than the traditional estimator based on the full model? The James-Stein estimator is such an estimator, having a property called minimaxity. However, the estimator considers only one reduced model, namely the origin. Hence it reduces...
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作者：Cai, TT; Low, MG
作者单位：University of Pennsylvania
摘要：Adaptive estimation of linear functionals over a collection of parameter spaces is considered. A between-class modulus of continuity, a geometric quantity, is shown to be instrumental in characterizing the degree of adaptability over two parameter spaces in the same way that the usual modulus Of Continuity captures the minimax difficulty of estimation over a single parameter space. A general construction of optimally adaptive estimators based on an ordered modulus of continuity is given. The r...
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作者：Ma, SG; Kosorok, MR
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：We consider partly linear transformation models applied to current status data. The unknown quantities are the transformation function, a linear regression parameter and a nonparametric regression effect. It is shown that the penalized MLE for the regression parameter is asymptotically normal and efficient and converges at the parametric rate, although the penalized MLE for the transformation function and nonparametric regression effect are only n(1/3) consistent. Inference for the regression ...
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作者：Einmahl, U; Mason, DM
作者单位：Vrije Universiteit Brussel; University of Delaware
摘要：We introduce a general method to prove uniform in bandwidth consistency of kernel-type function estimators. Examples include the kernel density estimator, the Nadaraya-Watson regression estimator and the conditional empirical process. Our results may be useful to establish uniform consistency of data-driven bandwidth kernel-type function estimators.
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作者：Guerre, E; Lavergne, P
作者单位：Sorbonne Universite; Universite PSL; Ecole des Hautes Etudes en Sciences Sociales (EHESS); Universite de Toulouse; Universite Toulouse 1 Capitole; Centre National de la Recherche Scientifique (CNRS)
摘要：We propose new data-driven smooth tests for a parametric regression function. The smoothing parameter is selected through a new criterion that favors a large smoothing parameter under the null hypothesis. The resulting test is adaptive rate-optimal and consistent against Pitman local alternatives approaching the parametric model at a rate arbitrarily close to I/root n. Asymptotic critical values come from the standard normal distribution and the bootstrap can be used in small samples. A genera...
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作者：Tiur, T
作者单位：Copenhagen Business School
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作者：Shah, KR; Bose, M; Raghavarao, D
作者单位：University of Waterloo; Indian Statistical Institute; Indian Statistical Institute Kolkata; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：We show that the balanced crossover designs given by Patterson [Biometrika 39 (1952) 32-48] are (a) universally optimal (UO) for the joint estimation of direct and residual effects when the competing class is the class of connected binary designs and (b) UO for the estimation of direct (residual) effects when the competing class of designs is the class of connected designs (which includes the connected binary designs) in which no treatment is given to the same subject in consecutive periods. I...
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作者：Bartlett, PL; Bousquet, O; Mendelson, S
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Max Planck Society; Australian National University
摘要：We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.
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作者：Lehmann, EL; Romano, JP
作者单位：University of California System; University of California Berkeley; Stanford University
摘要：H-1,..., H-s. The usual approach to dealing with the multiplicity problem is to restrict attention to procedures that control the familywise error rate (FWER), the probability of even one false rejection. In many applications, particularly if s is large, one might be willing to tolerate more than one false rejection provided the number of such cases is controlled, thereby increasing the ability of the procedure to detect false null hypotheses. This suggests replacing control of the FWER by con...
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作者：Reboul, L
作者单位：Universite de Poitiers; Universite Gustave-Eiffel
摘要：This paper deals with a nonparametric shape respecting estimation method for U-shaped or unimodal functions. A general upper bound for the nonasymptotic L-1-risk of the estimator is given. The method is applied to the shape respecting estimation of several classical functions, among them typical intensity functions encountered in the reliability field. In each case, we derive from our upper bound the spatially adaptive property of our estimator with respect to the L-1-metric: it approximately ...
