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.     

Conditional independence structure and its closure: Inferential rules and algorithms

In this paper, we deal with conditional independence models closed with respect to graphoid properties. Such models come from different uncertainty measures, in particular in a probabilistic setting. We study some inferential rules and describe methods and algorithms to compute efficiently the closure of a set of conditional independence statements.     

Conditional independence models for seemingly unrelated regressions with incomplete data

We consider normal ≡ Gaussian seemingly unrelated regressions (SUR) with incomplete data (ID). Imposing a natural minimal set of conditional independence constraints, we find a restricted SUR/ID model whose likelihood function and parameter space factor into the product of the likelihood functions and the parameter spaces of standard complete data multivariate analysis of variance models. Hence, the restricted model has a unimodal likelihood and permits explicit likelihood inference. In the development of our methodology, we review and extend existing results for complete data SUR models and the multivariate ID problem.     

Relating broadcast independence and independence

An independent broadcast on a connected graph $G$ is a function $f:V\left(G\right)\to {\mathbb{N}}_0$ such that, for every vertex $x$ of $G$, the value $f\left(x\right)$ is at most the eccentricity of $x$ in $G$, and $f\left(x\right)>0$ implies that $f\left(y\right)=0$ for every vertex $y$ of $G$ within distance at most $f\left(x\right)$ from $x$. The broadcast independence number ${\alpha }_b\left(G\right)$ of $G$ is the largest weight $\sum _{x\in V\left(G\right)}f\left(x\right)$ of an independent broadcast $f$ on $G$. Clearly, ${\alpha }_b\left(G\right)$ is at least the independence number $\alpha \left(G\right)$ for every connected graph $G$. Our main result implies ${\alpha }_b\left(G\right)\le 4\alpha \left(G\right)$. We prove a tight inequality and characterize all extremal graphs.     

条件独立性与条件回归独立性

引入随机变量条件独立、强条件回归独立、逐对条件回归独立、连续条件回归独立、邻对条件回归独立、累积条件回归独立诸定义,证明它们之间的等价关系.     

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。     

Monotonicity and independence axioms

Given σ, a family ofchoice problems, subsets ofR ⁿ representing the payoff vectors (measured in von Neumann-Morgenstern utility scales) attainable by a group ofn players, asolution f on σ associates to everyS in σ a unique elementf (S) ofS. Amonotonicity axiom specifies how the solution outcome should change when the choice problem is subjected to certain geometric transformations while anindependence axiom requires, in similar circumstances, the invariance of the solution outcome. A number of such axioms are here formulated and the logical relationships among them are established.Strong monotonicity is shown to be the strongest axiom and strongly monotonic solutions are characterized.     

Determining classes and independence

A class of functions of a random element X is sufficiently rich to determine the distribution of X if and only if it is sufficiently rich to determine whether X is independent of other random functions.     

双变量参数联合函数的条件均值和条件方差(英文)

联合函数是指连接单变量边际分布的多变量函数.联合函数由Sklar(1959)在概率测度空间的内容时引入的.本文主要对边际分布是标准正态分布函数U(0,1)的Farlie-Gum-bel-Morgenstern和Gumbel-Hougaard这两个双变量参数联合函数进行研究,我们得到了他们密度函数的基本性质并导出了他们的条件均值和条件方差.另外,本文还给出了不同参数的条件均值和条件方差的相应图示,并进行了对比和解释.     

Exponential independence

For a set $S$ of vertices of a graph $G$, a vertex $u$ in $V\left(G\right)?S$, and a vertex $v$ in $S$, let ${\mathrm{dist}}_{(G,S)}(u,v)$ be the distance of $u$ and $v$ in the graph $G?(S?\left\{v\right\})$. Dankelmann et al. (2009) define $S$ to be an exponential dominating set of $G$ if $w_{(G,S)}\left(u\right)\ge 1$ for every vertex $u$ in $V\left(G\right)?S$, where $w_{(G,S)}\left(u\right)={\sum }_{v\in S}{\left(\frac{1}{2}\right)}^{{\mathrm{dist}}_{(G,S)}(u,v)?1}$. Inspired by this notion, we define $S$ to be an exponential independent set of $G$ if $w_{(G,S?\left\{u\right\})}\left(u\right)<1$ for every vertex $u$ in $S$, and the exponential independence number ${\alpha }_e\left(G\right)$ of $G$ as the maximum order of an exponential independent set of $G$.Similarly as for exponential domination, the non-local nature of exponential independence leads to many interesting effects and challenges. Our results comprise exact values for special graphs as well as tight bounds and the corresponding extremal graphs. Furthermore, we characterize all graphs $G$ for which ${\alpha }_e\left(H\right)$ equals the independence number $\alpha \left(H\right)$ for every induced subgraph $H$ of $G$, and we give an explicit characterization of all trees $T$ with ${\alpha }_e\left(T\right)=\alpha \left(T\right)$.     

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。     

条件概率及其推广

本文在概率空间及事件域意义下解释通常意义下的条件概率定义，并借此形式推广了条件概率定义。     

Conditional measures and operators

A detailed study of the structure of conditional expectations and conditional probability measures is presented. Some characterizations of conditional expectations as a subclass of projection operators on Banach function spaces, and similarly conditional probabilities as a subclass of vector valued measures on such spaces are included. As applications of these results, a representation of Reynolds operators and related unified formulation of ergodic-martingale theorems are given.     

不等式证明的条件极值法

将不等式证明问题转化为求多元函数条件极值问题,并应用拉格朗日乘数法证明了Young不等式和Hlder不等式.     

Conditional pressure and coding

We use pressure to obtain invariants for bounded-to-one block homomorphisms between Markov shifts. These invariants enable us to show that if there is a bounded-to-one block homomorphism between Bernoulli shifts given by probability vectorsp andq thenq may be obtained fromp by a permutation. The invariants may be viewed as conditional pressures; a convergence theorem for eigenmeasures of Ruelle operators motivates the definition of conditional pressure and helps establish our invariants for regular isomorphism of Markov shifts. It follows that Bernoulli shifts given by probability vectorsp andq are regularly isomorphic iffq is a permutation ofp. We employ our invariants also in the context of a finite equivalence. Finally we indicate that ratio variational principles yield further invariants.     

On posets and independence spaces

By constructing a correspondence relationship between independence spaces and posets, under isomorphism, this paper characterizes loopless independence spaces and applies this characterization to reformulate certain results on independence spaces in poset frameworks. These state that the idea provided in this paper is a new approach for the study of independence spaces. We outline our future work finally.