三变量独立性的两个结论

本文给出了三随机变量相互独立与条件独立的两个结论．其中结论２表明，三变量如果两两条件独立，则三变量一定相互独立．     

模糊事件的条件λ─独立性

本文在文［３］的基础上，研究了模糊事件的条件λ─独立性的有关问题，给出了两个模糊事件条件λ─独立的充要条件，推广了概率论中的相应结果。     

三变量独立性的两个结论

本给出了三随机变量相互独立与条件独立的两个结论，其中结论2表明，三变量如果两两条件独立，则三变量一定相互独立。     

离散型随机变量的几种独立性及相互关系

随机变量的独立性在概率论中有着十分重要的意义.本文给出了离散型随机变量与离散型随机向量相互独立的概念,条件独立的概念,以及几种独立性的相互关系.     

条件自回归极差模型的渐近性质

在误差项独立同分布的条件下，本文讨论了条件自回归极差模型条件解和无条件解的渐近性质．利用随机游动的极限性质得到了条件解收敛于无条件解的充分条件，任意阶矩有限的充要条件以及外生变量与内生变量持续性的充要条件．所得到的结论适用于已得到应用的平稳条件自回归极差模型，也适用于包含单位根的模型和满足条件的其他类型的非平稳过程，为模型的统计推断提供了理论基础．     

追加数据对条件指数的影响

克服线性回归模型中设计矩阵的复共线性，主要有三种方法追加数据自变量选择和非量小二乘法，本文研究追加数据在减少条件数中的作用，我们的研究表明，在可能情况下适当地选择追加数据，设计矩阵的条件指数可以被减少。我们用在回归最优设计中广泛初研Ｇａｙｌｏｒ－Ｍｅｒｒｉｌｌ数据说明了本文理论结果的实用意义。     

连续随机变量的随机独立性与回归独立性

回归独立性是指给定随机变量 X时 ,随机变量 Y的条件期望 E( Y|X)不依赖于 X.前人讨论了离散型随机变量回归独立性与随机独立性的关系 ,得到了二者等价的充分必要条件 .对连续型随机变量的情形加以讨论 ,获到了二者等价的几个充分必要条件 ,并说明在统计分析中的应用 .     

公司规模门槛效应下的董事会独立性与公司价值的关系

对于独立董事比例与公司价值之间的关系,以往的研究得出了完全不同的两种结论,原因是因为公司在确定独立董事比例可能与公司规模等公司的自身因素大小。本研究通过检验发现,公司规模对独立董事比例具有门槛效果,使用门槛回归模型可以更好的解释独立董事比例和公司价值之间的关系,实证研究的结果表明,在不同公司规模门槛区间,独立董事比例和公司价值之间的相关性有所差异.     

相依风险条件下的医疗费用预测模型及其应用

在医疗费用的预测中,通常假设门诊费用与住院费用相互独立,事实上它们之间往往存在一定的相依关系。它们的相依性可以通过Copula函数来描述。在已知医疗费用发生的条件下,假设门诊费用和住院费用分别服从伽玛分布、逆高斯分布、对数正态分布和GB2分布,可以建立门诊费用与住院费用相互依赖的Copula回归模型。本文将此模型应用于一组实际的医疗费用数据,结果表明,考虑相依关系的回归模型要优于独立假设下的医疗费用预测模型。     

非对称广义自回归条件异方差的新模型

本文提出了一个新的非对称广义自回归条件异方差的新模型,证明了该模型宽平稳及其最简模型偶数价矩存在的充要条件。     

模型自由的稳健条件独立特征筛选

特征筛选方法对于超高维数据分析非常重要。本文基于Hoeffding’s独立检验统计量提出了一种新的条件独立筛选方法,简称为MMCSCIS。该方法具有以下特点:(1)不依赖于模型设定;(2)在自变量或因变量或条件变量的严格单调变换下结果不变;(3)可以同时处理条件特征筛选和特征筛选。通过模拟发现它对因变量或者自变量含有厚尾分布的数据和含有异常值的数据都比较稳健。最后我们通过两个实例分析说明了该方法的有效性。     

A Characterization of Exponential Distributions through Conditional Independence

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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.     

Residual-Based Shadings for Visualizing (Conditional) Independence

Residual-based shadings for enhancing mosaic and association plots to visualize independence models for contingency tables are extended in two directions: (a) perceptually uniform Hue-Chroma-Luminance (HCL) colors are used and (b) the result of an associated significance test is coded by the appearance of color in the visualization. For obtaining (a), a general strategy for deriving diverging palettes in the perceptually based HCL space is suggested. As for (b), cutoffs that control the appearance of color are computed in a data-driven way based on the conditional permutation distribution of maximum-type test statistics. The shadings are first established for the case of independence in two-way tables and then extended to more general independence models for multiway tables, including in particular conditional independence models.     

A Characterization of Geometric Distributions Through Conditional Independence

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条件期望与三变量独立性的两个充要条件

随机事件的独立性、随机变量的独立性是概率统计中的重要概念,不少学者都在这方面有所讨论.本文作者讨论了三维连续型随机变量（X,Y,Z）中三个分量X,Y,Z的相互独立性、条件独立性,得到三个引理.利用条件期望及三个引理作者给出了三变量相互独立的两个充要条件.     

A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data

Bayesian networks (BNs) have attained widespread use in data analysis and decision making. Well-studied topics include efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating BN probabilities, and structure learning. It is common for the researcher to assume the structure of the BN or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted BNs; this is the contribution of this article. We key on the definition of (conditional) independence to develop a graphical diagnostic that indicates whether the conditional independence assumptions imposed, when one assumes the structure of the BN, are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data with conditional independence assumptions under the model structure. We describe how this theoretical information and the data are presented in a graphical diagnostic tool. We demonstrate the approach through data simulated from BNs under different conditional independence assumptions. We also apply the diagnostic to a real-world dataset. The results presented in this article show that this approach is most feasible for smaller BNs—this is not peculiar to the proposed diagnostic graphic, but rather is related to the general difficulty of combining large BNs with data in any manner (such as through parameter estimation). It is the authors’ hope that this article helps highlight the need for more research into BN model diagnostics. This article has supplementary materials online.