一类随机游动上确界的密度的渐近性

Kluppelberg^[1], Asmussen 等^[2] 研究了增量有有限负均值的随机游动上确界的密度的渐近性. 该文则在 Denisov 等^[3], 程东亚和王岳宝^[4]的基础上, 进一步研究了增量均值为负无穷的随机游动上确界的密度的渐近性. 最后, 为了说明常见重尾分布大多满足上述结果的条件, 该文给出了一些分布族的性质.     

带有相依重尾冲击的Poisson噪音过程尾的一致渐近性质

本文考虑了带有某种相依重尾冲击的Poisson噪音过程尾的一致渐近性质.当冲击是二元上尾渐近独立的非负随机变量具有长尾和控制变化尾分布且噪音函数具有正的上下界时,得到了过程尾概率的一致渐近公式.进而,当冲击具有连续的一致变化尾分布时,去除了噪音函数具有正的下界的限制.对于噪音函数不一定具有正的上界的情形,当冲击具有两两负象限相依结构时,也得到了一致渐近性结果.     

带不同分布增量的随机和的局部渐近性

在Asmussen,Foss and Korshunov(J.Theoretical Probab.,2003,16(2)489-518)的基础上,讨论了支撑在(-∞,∞)上的不同分布的卷积的封闭性及带上述不同分布增量的局部渐近性.上述分布包括了常见的轻尾分布和重尾分布.     

带不同分布增量的随机和的局部渐近性

在Asmussen,Foss and Korshunov(J.Theoretical Probab.,2003,16(2):489-518)的基础上,讨论了支撑在(-∞,∞)上的不同分布的卷积的封闭性及带上述不同分布增量的局部渐近性.上述分布包括了常见的轻尾分布和重尾分布.     

一类随机加权和的渐近性及其应用

本文考虑随机加权和及其最大值尾概率的渐近性,其中增量{X_i,i≥1}为一列独立同分布的实值随机变量,权重{θ_i,i≥1}为另一列非负的随机变量,并且两列随机变量满足某种相依结构.在增量的共同分布F属于控制变换分布族的条件下,我们得到了随机加权和及其最大值尾概率的弱渐近等价估计.特别地,当F属于一致变换分布族时,得到了渐近等价估计.最后,我们将该结果应用于破产概率的渐近估计.     

随机删失下极大似然估计的局部渐近正态与渐近极小极大有效

研究观察数据被随机右删失时 ,参数分布族的局部渐近正态与渐近极小极大有效 .建立局部渐近正态成立的充分条件 ,给出渐近极小极大风险的下界以及达到该下界的充分必要条件 ,并证明随机删失下参数极大似然估计的渐近极小极大有效.     

一类重尾分布下的随机游动的渐近结果及其在风险理论中的应用

定义了一类新的重尾分布族し*(m),并研究了该分布族的若干性质,讨论了在该类分布族下的(非标准)随机游动{Sn}的尾概率的渐近性质,并给出了在风险理论中的应用.     

一类非线性脉冲泛函微分方程解的渐近性

利用构造Lyapunov泛函方法讨论了一类具有正负系数的非线性脉冲泛函微分方程解的渐近性,给出方程的解趋于一个正常数的充分条件,并举例说明定理条件和结论的可实现性.     

上尾渐近独立随机变量和的大偏差的渐近下界(英文)

本文研究了多元风险模型中服从长尾分布的带上尾渐近独立的随机变量和的大偏差渐近下界.利用大偏差的经典求法,得到了随机变量的非随机和和随机和的大偏差表达式,推广了独立同分布情形下的相关结论.     

时间相依更新风险模型中无限时绝对破产概率的渐近性

本文考虑了两类时间相依且带常利率和常值保费收入率的更新风险模型的无限时绝对破产概率, 其中索赔额及其到达时间间隔构成独立同分布的随机对列, 以及每个随机对遵循某种相依结构. 基于此, 当索赔额分布属于R_-∞∩J(γ), γ > 0 分布族时, 我们分别得到了两类时间相依结构下的无限时绝对破产概率的渐近公式和渐近上界.     

Spectral Asymptotics Revisited

We discuss two heuristic ideas concerning the spectrum of a Laplacian, and we give theorems and conjectures from the realms of manifolds, graphs and fractals that validate these heuristics. The first heuristic concerns Laplacians that do not have discrete spectra: here we discuss the notion of ??spectral mass?? closely related to the ??integrated density of states??, an average of the diagonal of the kernel of the spectral projection operator, and show that this can serve as a substitute for the eigenvalue counting function. The second heuristic is an ??asymptotic splitting law?? that describes the proportions of the spectrum that transforms according to the irreducible representations of a finite group that acts as a symmetry group of the Laplacian. For this to be valid we require the existence of a fundamental domain with relatively small boundary. We also give a version in the case that the symmetry group is a compact Lie group. Many of our results are reformulations of known results, and some are merely conjectures, but there is something to be gained by looking at them together with a unified perspective.     

Asymptotics of Analytic Semigroups

No Abstract. June 2, 1999     

Asymptotics for Two-Dimensional Atoms

We prove that the ground state energy of an atom confined to two dimensions with an infinitely heavy nucleus of charge Z > 0 and N quantum electrons of charge −1 is E(N,Z)=-\frac12Z²ln Z+(E^TF(l)+\frac12c^H)Z²+o(Z²){E(N,Z)=-\frac{1}{2}Z^2{\rm ln} Z+(E^{\rm TF}(\lambda)+\frac{1}{2}c^{\rm H})Z^2+o(Z^2)} when Z → ∞ and N/Z → λ, where E ^TF(λ) is given by a Thomas–Fermi type variational problem and c ^H ≈ −2.2339 is an explicit constant. We also show that the radius of a two-dimensional neutral atom is unbounded when Z → ∞, which is contrary to the expected behavior of three-dimensional atoms.     

Asymptotics of random contractions

In this paper we discuss the asymptotic behaviour of random contractions X=RS, where R, with distribution function F, is a positive random variable independent of S∈(0,1). Random contractions appear naturally in insurance and finance. Our principal contribution is the derivation of the tail asymptotics of X assuming that F is in the max-domain of attraction of an extreme value distribution and the distribution function of S satisfies a regular variation property. We apply our result to derive the asymptotics of the probability of ruin for a particular discrete-time risk model. Further we quantify in our asymptotic setting the effect of the random scaling on the Conditional Tail Expectations, risk aggregation, and derive the joint asymptotic distribution of linear combinations of random contractions.     

Asymptotics of ACH-Einstein Metrics

We study the boundary asymptotics of ACH metrics which are formally Einstein. In terms of the partially integrable almost CR structure induced on the boundary at infinity, existence and uniqueness of such formal asymptotic expansions are studied. It is shown that there always exist formal solutions to the Einstein equation if we allow logarithmic terms, and that a local CR-invariant tensor arises as the obstruction to the existence of a log-free solution. Some properties of this new CR invariant, the CR obstruction tensor, are discussed.     

Asymptotics of q-plancherel measures

In this paper, we are interested in the asymptotic size of the rows and columns of a random Young diagram under a natural deformation of the Plancherel measure coming from Hecke algebras. The first lines of such diagrams are typically of order n, so it does not fit in the context of the work of Biane (Int Math Res Notices 4:179–192, 2001) and ?niady (Probab. Theory Relat Fields 136:263–297, 2006). Using the theory of polynomial functions on Young diagrams of Kerov and Olshanski, we are able to compute explicitly the first- and second-order asymptotics of the length of the first rows. Our method also works for other measures, for example those coming from Schur–Weyl representations.     

Asymptotics of generalized derangements

An adaptive wavelet-based method is proposed for solving TV(total variation)–Allen–Cahn type models for multi-phase image segmentation. The adaptive algorithm integrates (i) grid adaptation based on a threshold of the sparse wavelet representation of the locally-structured solution; and (ii) effective finite difference on irregular stencils. The compactly supported interpolating-type wavelets enjoy very fast wavelet transforms, and act as a piecewise constant function filter. These lead to fairly sparse computational grids, and relax the stiffness of the nonlinear PDEs. Equipped with this algorithm, the proposed sharp interface model becomes very effective for multi-phase image segmentation. This method is also applied to image restoration and similar advantages are observed.