条件VaR和条件CVaR的核估计及其实证分析

VaR和CVaR是目前两种主流风险度量工具。条件VaR和条件CVaR是基于市场风险因子在已知条件(或信息)下的分布来计量和测算VaR和CVaR,能够及时地根据变化的条件来重新估计风险进而进行有效的风险管理,是对传统的基于边际分布的VaR和CVaR指标的有益补充。另外一方面,近年来非参数核估计方法因模型设定灵活、方便处理变量相依结构等优点备受关注。在本文,我们用条件VaR和条件CVaR的非参数核估计法,对我国A股市场的风险进行测算。结果得出:条件VaR和条件CVaR能揭示出深证成指和上证综指之间的不同风险特征;条件VaR和条件CVaR的测算结果并非总是一致;系统风险估计值对已知条件的敏感性高于深发展A和万科A两只股票的个股风险。以上风险特征在边际VaR和边际CVaR下无法得到。     

获得参数最短置信区间长度的条件

寻找统计分布中参数的最短置信区间长度往往不容易,一些文献往往讨论具体分布中参数的最短置信区间长度.本文从常用枢轴变量的形式即参数的线性函数形式和反比例函数形式出发,可以获得得到参数最短置信区间长度的两个条件,并且枢轴变量的密度函数满足一定条件时,最短置信区间长度是存在且唯一的,结论具有一般性.     

IIRCT下泊松分布参数多变点模型的贝叶斯估计

通过添加缺损的寿命变量数据得到了带有不完全信息随机截尾试验下泊松分布参数多变点模型的完全数据似然函数,研究了变点位置参数和其它参数的满条件分布.利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法对各参数的满条件分布分别进行了抽样,把Gibbs样本的均值作为各参数的贝叶斯估计,并且详细介绍了MCMC方法的实施步骤.最后进行了随机模拟试验,试验结果表明各参数贝叶斯估计的精度都较高.     

方差函数确定指数族的条件

本文以矩量问题的分布函数解为工具,给出了方差函数(V(m),M_0)确定REF(V(m),M)的充分必要条件.此外,作者还得到矩量问题有限支撑分布函数解存在的一个充分必要条件.     

带厚尾新息的非线性自回归函数型条件异方差模型的尾概率

研究带厚尾新息的非线性自回归函数型条件异方差(NARFCH)模型的平稳分布的尾概率. 结果表明, NARFCH序列的平稳分布的尾部紧密依赖于其条件方差. 当新息序列呈厚尾分布时, NARFCH序列的平稳分布的尾部会比新息序列的尾部更厚或更薄, 给出了具体的尾概率的增加或减少对条件方差的依赖公式, 并给出了两个具体例子来说明主要结果的应用.     

基于条件分布函数的高维回归模型的变量选择方法

高维回归分析的变量选择问题是目前统计学研究的一个热点和难点问题.提出了一个基于条件分布函数的相关性度量准则,并在此基础上提出三种变量选择方法.与现有的方法相比,提出的方法不依赖于统计模型,可以适用于线性模型和非参数可加模型.数值模拟结果表明,即使协变量之间存在一定的相关性,方法也有较为满意的表现.     

全局优化问题的一类积分型最优性条件 (英)

本文通过构造水平集辅助函数对一类积分全局最优性条件进行研究. 所构造的辅助函数仅含有一个参数变量与一个控制变量,该参数变量用以表征对原问题目标函数最优值的估计,而控制变量用以控制积分型全局最优性条件的精度. 对参数变量做极限运算即可得到积分型全局最优性条件.继而给出了用该辅助函数所刻画的全局最优性的充要条件, 从而将原全局优化问题的求解转化为寻找一个非线性方程根的问题.更进一步地,若所取测度为勒贝格测度且积分区域为自然数集合的一个有限子集, 则该积分最优性条件便化为有限极大极小问题中利用凝聚函数对极大值函数进行逼近的近似系统.从而积分型全局最优性条件可以看作是该近似系统从离散到连续的一种推广.     

删失截断情形下Weibull分布多变点模型的参数估计

通过添加缺失的寿命变量数据,得到了删失截断情形下Weibull分布多变点模型的完全数据似然函数,研究了变点位置参数和形状参数以及尺度参数的满条件分布.利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法得到了参数的Gibbs样本,把Gibbs样本的均值作为各参数的Bayes估计.详细介绍了MCMC方法的实施步骤.随机模拟试验的结果表明各参数Bayes估计的精度都较高.     

应用曲线积分求解条件概率密度函数

本文将随机变量X在随机事件■(其中f(X)为线性函数)的条件下的概率分布转化为联合密度函数在该曲线段上的曲线积分,研究了在随机事件B发生的条件下随机变量X的条件概率密度函数.同时,本文通过例证说明,通过曲线积分求解条件概率密度函数较之从条件分布定义的求解方式更为简洁.     

空间非参回归的变量选择

空间变系数回归模型是空间线性回归模型的重要推广,在实际中有广泛的应用.然而,这个模型的变量选择问题还没有解决.本文通过一般的M型损失函数将均值回归、中位数回归、分位数回归和稳健均值回归纳入同一框架下,然后基于B样条近似,提出一个能够同时进行变量选择和函数系数估计的自适应组内(adaptive group)L_r(r≥1)范数惩罚的M型估计量.新方法有几个显著的特点:(1)对异常点和重尾分布稳健;(2)能够兼容异方差性,允许显著变量集合随所考虑的分位点不同而变化;(3)兼顾了估计量的有效性和稳健性.在较弱假设条件下,建立了变量选择的oracle性质.随机模拟和实例分析验证了所提方法在有限样本时的表现.     

基于条件事件代数系统的条件证据组合与条件信任组合

讨论基于条件事件代数系统上的条件证据与条件信任问题,给出了条件证据的组合方法,由此可作出在条件下对目标支持的判断,我们还给出了综合各条件证据的无条件目标语据组合,它可看作是从全局角度对目标支持的判断,最后我们给出了基于多值映射的条件信任的组合。     

ψ-混合样本下含附加信息时条件分位数估计的渐近性质

本文利用经验似然方法构造了含附加信息时条件分位数的一类估计,并证明了估计的渐近正态性且渐近方差不大于通常核估计的渐近方差.     

Conditional versions of limit theorems for conditionally associated random variables

Relation between association and conditional association is answered, several examples show that the association of random variables does not imply the conditional association, and vice versa. Several fundamental properties of conditional associated random variables are developed, which extend the corresponding ones under the non-conditioning setup. By means of these properties, some conditional Hájek-Rényi type inequalities, a conditional strong law of large numbers and a conditional central limit theorem stated in terms of conditional characteristic functions are established, which are conditional versions of the earlier results for associated random variables, respectively. In addition, some lemmas in the context are of independent interest.     

Conditional colorings of graphs

For an integer r>0, a conditional(k,r)-coloring of a graph G is a proper k-coloring of the vertices of G such that every vertex of degree at least r in G will be adjacent to vertices with at least r different colors. The smallest integer k for which a graph G has a conditional (k,r)-coloring is the rth order conditional chromatic number χ_r(G). In this paper, the behavior and bounds of conditional chromatic number of a graph G are investigated.     

On Conditional Independence,Mixing, and Association

Abstract

This is a follow-up to a recent article by Prakasa Rao [15 Prakasa Rao , B.L.S. 2008 . Conditional independence, conditional mixing and association . Annals of the Institute of Statistical Mathematics AISM , doi: 10.1007/S10463-007-0152-2 . [Google Scholar]] on conditional independence, conditional mixing and conditional association. The purpose of this article is to derive rigorously some results following from conditioning. To this end, a brief review is presented of the concepts of conditional independence of events, classes of events, and random variables, followed by a conditional version of a factorization theorem, as well as a first installment of some basic results. Next, the concepts of conditional covariance and variance are introduced, and a second installment of basic results follows. Furthermore, a certain representation of the covariance is established in detail, followed by a conditional version of it, as well as a generalization. The concept of the conditional characteristic function is also recalled, and a certain inequality is established. Finally, the concept of conditional positive (negative) quadrant dependence, as well as that of conditional positive (negative) association are introduced. The article concludes with the derivation of the conditional versions of some known results, regarding positive (negative) association. This is done anticipating that conditional association (and also conditional mixing) will prove to be of significant applicability.     

Conditional independence,conditional mixing and conditional association

Some properties of conditionally independent random variables are studied. Conditional versions of generalized Borel-Cantelli lemma, generalized Kolmogorov’s inequality and generalized Hájek-Rényi inequality are proved. As applications, a conditional version of the strong law of large numbers for conditionally independent random variables and a conditional version of the Kolmogorov’s strong law of large numbers for conditionally independent random variables with identical conditional distributions are obtained. The notions of conditional strong mixing and conditional association for a sequence of random variables are introduced. Some covariance inequalities and a central limit theorem for such sequences are mentioned.     

半群上条件正定函数的扩张

讨论半群上条件正定函数的扩张问题.得到一个扩张定理.作为应用,得到有界扩张定理和Pontryagin空间上量子力学的一个基本定理.     

Conditional tests for elliptical symmetry

In this paper, we suggest the conditional test procedures for testing elliptical symmetry of multivariate distribution. The conditional tests are exactly valid if the symmetric center and the shape matrix are given and are asymptotically valid if they are unknowns to be estimated. The equivalence, in the large sample sense, between the conditional tests and their unconditional counterparts is established. The power behavior of the tests under global as well as local alternatives is investigated theoretically. A small simulation study is performed.     

Chow-Type Maximal Inequality for Conditional Demimartingales and Its Applications

In this paper, the Chow-type maximal inequality for conditional demimartingales is established. By using the Chow-type maximal inequality, the authors provide the maximal inequality for conditional demimartingales based on {concave Young functions}. At last, the moment inequalities for conditional demimartingales are established.     

Conditional p-adic probability logic

In this paper we present the proof-theoretical approach to p-adic valued conditional probabilistic logics. We introduce two such logics denoted by ${\mathit{CPL}}_{{\mathbf{Z}}_{\mathbf{p}}}$ and ${\mathit{CPL}}_{{\mathbf{Q}}_{\mathbf{p}}}^{\mathit{fin}}$. Each of these logics extends classical propositional logic with a list of binary (conditional probability) operators. Formulas are interpreted in Kripke-like models that are based on p-adic probability spaces. Axiomatic systems with infinitary rules of inference are given and proved to be sound and strongly complete. The decidability of the satisfiability problem for each logic is proved.