Hermite—Fejer插值于Lp下的收敛逼近阶

本文把文［1—3」等仅对P≤4给予证明的P．Erdos－Feldheim型定理给出了一个完整的证明，且把文［1］的结果作了改进．     

A Note on the Strong Approximation of the Smoothed Empirical Process of α-mixing Sequences

A strong approximation of the smoothed empirical process of strictly stationary α-mixing random variables by a sequence of iid Gaussian processes will be studied. Here, the smoothing is done via kernel density estimators. No assumptions are made on the support of the kernel; in fact, our main results are stated for kernels with possibly an infinite support. Received June 2003; Accepted February 2004.     

保费依赖向后重现时间过程风险模型的破产概率上界(英文)

本文研究带利率的风险模型,它的索赔计数过程是一个更新计数过程,保费收入依赖于向后重现时间过程.通过鞅方法和递推技术,得到破产概率的两个指数型上界.最后,还研究了几个具体的例子,并且给出上界的数量比较.     

Effects of Smoothing on Multivariate Statistical Properties of the Normal to a Rough Curve or Surface

Much of the practical interest attached to curves and surfaces derives from features of roughness, rather than smoothness. For example, considerable attention has been paid to fractal models of curves and surfaces, for which the notions of a normal and curvature are usually not well defined. Nevertheless, these quantities are sometimes measurable, because the device for recording a rough surface (such as a stylus or “compass”) adds its own intrinsic smoothness. In this paper we address the effect of such smoothing operations on the multivariate statistical properties of a normal to the surface. Particular attention is paid to the validity of commonly assumed unimodal approximations to the distribution of the normal. It is shown that the actual distribution may have more than one mode, although in a range of situations the unimodal approximation is valid.     

Approximation of functions on the real axis by Féjer-type operators in the generalized Hölder metric

In this paper, we consider the orders of approximation of functions on the whole real axis by operators of Fejér type in the Banach space with the so-called generalized Hölder metric.     

An integro-local theorem applicable on the whole half-axis to the sums of random variables with regularly varying distributions

We obtain an integro-local limit theorem for the sum S(n) = ξ(1)+?+ξ(n) of independent identically distributed random variables with distribution whose right tail varies regularly; i.e., it has the form P(ξ≥t) = t ^?β L(t) with β > 2 and some slowly varying function L(t). The theorem describes the asymptotic behavior on the whole positive half-axis of the probabilities P(S(n) ∈ [x, x + Δ)) as x → ∞ for a fixed Δ > 0; i.e., in the domain where the normal approximation applies, in the domain where S(n) is approximated by the distribution of its maximum term, as well as at the “junction” of these two domains.     

An Upper Bound on the Number of Iterations for Transforming a Boolean Function of Degree Greater or Equal Than 4 to a Function of Degree 3

The difficulty involved in characterizing the weight distribution of all Boolean functions of degree 3 is well-known [2, p. 446]. In [1] the author introduces a transformation on Boolean functions which changes their weights in a way that is easy to follow, and which, when iterated, reduces the degree of the function to 2 or 3. He concludes that it is just as difficult to characterize the weight of any function of degree 3 as it is for any other degree. The application of this transformation on a Boolean function defined on , increases the number of its variables by two. On the other hand, in order to reduce the degree of a function to 2 or 3 it is necessary to apply the tranformation a number of times that grows exponentially with respect to m. In this paper, a factorization method on Boolean functions that allows the establishment of an upper bound for the number of applications of the transformation is presented. It shows that, in general, it is possible to significantly decrease the number of iterations in this process of degree reduction.     

An Upper and Lower Solution Theory for the Problem （G＇（y））＇ ＋ f（t,y）= 0 on Finite and Infinite Intervals

A new upper and lower solution theory is presented for the second order problem （G＇（y））＇＋ f（t, y） = 0 on finite and infinite intervals. The theory on finite intervals is based on a Leray-Schauder alternative, where as the theory on infinite intervals is based on results on the finite interval and a diagonalization process.     

Testing the tail–dependence based on the radial component

Throughout this article we assume that the df H of a random vector (X,Y) is in the max-domain of attraction of an extreme value distribution function (df) G with reverse exponential margins. Therefore, the asymptotic dependence structure of H can be represented by a Pickands dependence function D with D = 1 representing the case of asymptotic independence. One of our aims is to test the null hypothesis of tail-dependence against the alternative of tail-independence. Thus we want to prove the validity of the model where D = 1. The test is based on the radial component X + Y. Under a certain spectral expansion it is verified that the df of X + Y, conditioned on X + Y > c, converges to F(t) = t, as c ↑0, if D ≠ 1 and, respectively, to F(t) = t ^1 + ρ , if D = 1, where ρ > 0 determines the rate at which independence is attained. Based on the limiting dfs we find a uniformly most powerful test procedure for testing tail-dependence against rates of tail-independence. In addition, an estimator of the parameter ρ is proposed. The relationship of ρ to another dependence measure, given in the literature, is indicated.