Estimation of the marginal expected shortfall under asymptotic independence

We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby.     

Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp-optimization

The conditional tail expectation (CTE) is an indicator of tail behavior that takes into account both the frequency and magnitude of a tail event. However, the asymptotic normality of its empirical estimator requires that the underlying distribution possess a finite variance; this can be a strong restriction in actuarial and financial applications. A valuable alternative is the median shortfall (MS), although it only gives information about the frequency of a tail event. We construct a class of tail L^p-medians encompassing the MS and CTE. For p in (1,2), a tail L^p-median depends on both the frequency and magnitude of tail events, and its empirical estimator is, within the range of the data, asymptotically normal under a condition weaker than a finite variance. We extrapolate this estimator and another technique to extreme levels using the heavy-tailed framework. The estimators are showcased on a simulation study and on real fire insurance data.     

A Semi-parametric Regression Model with Errors in Variables

Abstract.  In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non-parametric kernel estimator. Strong convergence, optimal rate of weak convergence and asymptotic normality of the estimators are investigated.     

二阶非线性微分方程解的振动性和渐近性

讨论了二阶非线性常微分方程(α(t)(?)(x)x′)′+q(t)f(x)=r(t)的解的振动性和渐近性.获得了有关该方程的五个新的定理.     

形式美感:从正常形式到超常形式

视觉艺术的形式美感研究是对美术本体的研究 ,它分为不同质的三个层次 ,即正常形式、反常形式和超常形式。“正常形式”具有自然真实、传统规范和流行图式的结构性特征 ,这是一个基础平台。“反常形式”是对正常形式的解构 ,需要发散思维 ,探索尽可能多的形式可能性 ,这是思维方式和审美趣味的转折点。“超常形式”是对反常形式和正常形式的个人化整合 ,目标是重构“个人化的超常形式” ,占据视觉艺术的制高点     

网络零售配送系统最优运作成本的分析

针对配送运作成本关键部分棗平均运距作出的估计,在车辆路径问题渐进最优理论的基础上,建立了一个平均运作成本最优的规划模型,从经济的角度对运作策略作了简要分析。与其他规划模型不同,新建模型考虑了平均水平的概率特性及众多实际因素,因而具有较好的实用性。     

Gini’s nuclear family

The purpose of this paper is to justify the use of the Gini coefficient and two close relatives for summarizing the basic information of inequality in distributions of income. To this end we employ a specific transformation of the Lorenz curve, the scaled conditional mean curve, rather than the Lorenz curve as the basic formal representation of inequality in distributions of income. The scaled conditional mean curve is shown to possess several attractive properties as an alternative interpretation of the information content of the Lorenz curve and furthermore proves to yield essential information on polarization in the population. The paper also provides asymptotic distribution results for the empirical scaled conditional mean curve and the related family of empirical measures of inequality.      

板模型具部分反射边界条件的迁移算子的渐近点谱及其聚点

研究非均匀介质、各向异性和连续能量的板模型迁移算子A在部分反射边界条件下的渐近点谱及其聚点.在F (1≤P< ∞)空间获得了算子A的渐近点谱以及谱聚点的分布等新的结果.     

一类Kermack—Mckandrick方程组初值问题

本文研究描述传染病现象的一类Kermack—Mekandrick方程组初值问题光滑解整体存在唯一性及渐近性质。