3期

最新文章

• Approximating conditional distribution functions using dimension reduction AI翻译
  作者：Hall, P; Yao, QW
  作者单位：Australian National University; University of London; London School Economics & Political Science
  摘要：Motivated by applications to prediction and forecasting, we suggest methods for approximating the conditional distribution function of a random variable Y given a dependent random d-vector X. The idea is to estimate not the distribution of Y vertical bar X, but that of Y vertical bar theta(T)X' where the unit vector theta is selected so that the approximation is optimal under a least-squares criterion. We show that theta may be estimated root-n consistently, Furthermore, estimation of the cond...
• Optimal testing of equivalence hypotheses AI翻译
  作者：Romano, JP
  作者单位：Stanford University
  摘要：In this paper we consider the construction of optimal tests of equivalence hypotheses. Specifically, assume X-1,..., X-n are i.i.d. with distribution P theta, with theta is an element of R-k. Let g(theta) be some real-valued parameter of interest. The null hypothesis asserts g(theta) is an element of (a, b) versus the alternative g(theta) is an element of (a, b). For example, such hypotheses occur in bioequivalence studies where one may wish to show two drugs, a brand name and a proposed gener...
• Breakdown and groups - Rejoinder AI翻译
  作者：Davies, PL; Gather, U
  作者单位：University of Duisburg Essen; Dortmund University of Technology
• On optimality of stepdown and stepup multiple test procedures AI翻译
  作者：Lehmann, EL; Romano, JP; Shaffer, JP
  作者单位：University of California System; University of California Berkeley; Stanford University
  摘要：Consider the multiple testing problem of testing k null hypotheses, where the unknown family of distributions is assumed to satisfy a certain monotonicity assumption. Attention is restricted to procedures that control the familywise error rate in the strong sense and which satisfy a monotonicity condition. Under these assumptions, we prove certain maximin optimality results for some well-known stepdown and stepup procedures.
• Breakdown and groups - Discussion AI翻译
  作者：Tyler, DE
  作者单位：Rutgers University System; Rutgers University New Brunswick
• Square root penalty: Adaptation to the margin in classification and in edge estimation AI翻译
  作者：Tsybakov, AB; van de Geer, SA
  作者单位：Sorbonne Universite; Universite Paris Cite; Leiden University; Leiden University - Excl LUMC
  摘要：We consider the problem of adaptation to the margin in binary classification. We suggest a penalized empirical risk minimization classifier that adaptively attains, up to a logarithmic factor, fast optimal rates of convergence for the excess risk, that is, rates that can be faster than n(-1/2), where n is the sample size. We show that our method also gives adaptive estimators for the problem of edge estimation.
• Nonparametric checks for single-index models AI翻译
  作者：Stute, W; Zhu, LX
  作者单位：Justus Liebig University Giessen; University of Hong Kong
  摘要：In this paper we study goodness-of-fit testing of single-index models. The large sample behavior of certain score-type test statistics is investigated. As a by-product, we obtain asymptotically distribution-free maximin tests for a large class of local alternatives. Furthermore, characteristic function based goodness-of-fit tests are proposed which are omnibus and able to detect peak alternatives. Simulation results indicate that the approximation through the limit distribution is acceptable a...
• Uniform in bandwidth consistency of kernel-type function estimators AI翻译
  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.
• Generalizations of the familywise error rate AI翻译
  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...
• Estimation of a function under shape restrictions. Applications to reliability AI翻译
  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 ...