条件经验随机过程的渐近性质

设(Xi,Yi)(i=1,2,…,n)是来自总体(X,Y)的样本(独立同分布),其中X∈R1,Y∈Rq.M(x y)是Y=y时X的条件分布,Mnkn(x y)为M(x y)的第kn个最近邻域的经验分布估计量,讨论条件经验过程Sn(t,x,y)=kn12(Mnkn(x y)-M(x y))的渐近性质,得出在适当条件下,对固定的y,Sn(t,x,y)(x,t为参数)弱收敛于某一G aussian过程S(.).     

Extremes of Normed Empirical Moment Generating Function Processes

We present a method for deriving the limiting distribution of the maximum of a normed empirical moment generating function process indexed by one parameter. We first extend slightly the results of Csörg et al. (1986b) to provide the rate of convergence for a Gaussian approximation to a non-Donsker empirical process. In cases we consider, the maximum tends to infinity in probability, but when appropriately scaled has a limiting Gumbel extreme value distribution.AMS 2000 Subject Classification Primary—62E20, 60G70*Author for correspondence: School of Mathematics and Statistics, University of Sydney, New South Wales 2006, Australia.     

Set-Indexed Conditional Empirical and Quantile Processes Based on Dependent Data

We consider a conditional empirical distribution of the form F_n(C x)=∑ⁿ_t=1 ω_n(X_t−x) I{Y_tC} indexed by C , where {(X_t, Y_t), t=1, …, n} are observations from a strictly stationary and strong mixing stochastic process, {ω_n(X_t−x)} are kernel weights, and is a class of sets. Under the assumption on the richness of the index class in terms of metric entropy with bracketing, we have established uniform convergence and asymptotic normality for F_n(· x). The key result specifies rates of convergences for the modulus of continuity of the conditional empirical process. The results are then applied to derive Bahadur–Kiefer type approximations for a generalized conditional quantile process which, in the case with independent observations, generalizes and improves earlier results. Potential applications in the areas of estimating level sets and testing for unimodality (or multimodality) of conditional distributions are discussed.     

应用积分经验过程检验多元分布函数的相等性

本文引进投影积分经验过程用于检验两个或K个多元分布函数的相等性，自助法用于确定临界值的逼近，数论方法有效地计算自动法确定的临界值，且进行了一些模拟试验．     

基于负相协样本经验过程的加权弱收敛

在文中,我们建立了基于负相协样本经验过程的加权弱收敛,同时利用这个结果,我们还证明了其积分的弱收敛性,并将这些结果应用于生存分析,获得屯一些相应的弱极限性质。     

NA序列下经验分布函数的渐近正态性及其应用

本文在NA负相协序列下利用熟知的相依情形的大小块分割的方法,建立了经验分布函数的渐近正态性．作为在可靠性中的应用,得到了生存函数■(x)=P(X＞x)估计的渐近正态性．     

Empirical likelihood estimation of discretely sampled processes of OU type

This paper presents an empirical likelihood estimation procedure for parameters of the discretely sampled process of Ornstein-Uhlenbeck type. The proposed procedure is based on the condi- tional characteristic function, and the maximum empirical likelihood estimator is proved to be consistent and asymptotically normal. Moreover, this estimator is shown to be asymptotically efficient under some mild conditions. When the background driving Lévy process is of type A or B, we show that the intensity parameter c...     

绝对值损失下单边截断型分布族参数的经验Bayes估计

本文在绝对值损失下,构造了单边截断型分布族参数的EB估计,并证明了在一组条件下,其Bayes风险的收敛速度为0((ln n/n)~(λγ/(2r+))·M_n),其中0<λ,γ≤1,M_n≤ln ln n(n充分大),M_n为一无穷大量。     

Nonparametric Estimation of the Dependence Function in Bivariate Extreme Value Distributions

The paper considers the problem of estimating the dependence function of a bivariate extreme survival function with standard exponential marginals. Nonparametric estimators for the dependence function are proposed and their strong uniform convergence under suitable conditions is demonstrated. Comparisons of the proposed estimators with other estimators are made in terms of bias and mean squared error. Several real data sets from various applications are used to illustrate the procedures.     

Goodness-of-Fit Tests for the Cauchy Distribution Based on the Empirical Characteristic Function

Let X ₁,...,X _n be independent observations on a random variable X. This paper considers a class of omnibus procedures for testing the hypothesis that the unknown distribution of X belongs to the family of Cauchy laws. The test statistics are weighted integrals of the squared modulus of the difference between the empirical characteristic function of the suitably standardized data and the characteristic function of the standard Cauchy distribution. A large-scale simulation study shows that the new tests compare favorably with the classical goodness-of-fit tests for the Cauchy distribution, based on the empirical distribution function. For small sample sizes and short-tailed alternatives, the uniformly most powerful invariant test of Cauchy versus normal beats all other tests under discussion.     

On the Maximum of Some Conditional and Integrated Gaussian Fields and their Statistical Applications

We evaluate upper bounds for the maximal distributions of some Gaussian random fields, which arise in the study of the asymptotic behavior of various two-dimensional empirical processes, with random index. Some of them are generalizations of well-known conditional Brownian fields, while the others are obtained by their integration. We present also some possible statistical applications of our results.