NMAR机制下非线性均值方差模型的Bayes估计

讨论响应变量带有不可忽略缺失数据的非线性均值方差模型的Bayes估计问题.缺失数据机制由logistic回归模型来指定,运用Gibbs抽样及MH算法得到模型参数和缺失数据机制参数的联合Bayes估计,模拟研究和实例分析展示上述模型和方法的可行性.     

关于数据缺失机制的检验方法探讨

在调查研究中,缺失数据是一个非常普遍的问题,各种处理缺失数据的方法都是建立在数据缺失机制的某种假定上.在总结他人研究成果的基础上,分别给出了MCAR、MAR和NMAR机制的检验识别方法,MCAR机制的检验从分布特征入手,通过比较均值和方差是否一致来判定;MAR机制的检验利用Logit模型刻画缺失指示变量R的分布,通过估计参数的显著性来判定,NMAR机制则通过对数据的缺失模式和原因进行分析来识别.     

数据缺失条件下我国教育收益率估计和分析

教育收益率是指受教育者因增加其受教育的数量而得到的未来净经济报酬的一种测量,它是评价教育生产力的一个非常有用的指数。然而,在收集个人收入数据时,收入往往存在缺失,这为正确估计教育收益率带来了很大的障碍。本文基于我国城镇职工在2002年的年收入数据,在不可忽略缺失假设下,使用明瑟收入函数,研究了我国当年的教育收益率。本文采用Logisitic模型对缺失机制进行拟合,并用联合似然函数方法推断模型中的所有未知参数。该联合似然函数中的参数具有可识别性。假设估计量是相合的,我们证明了估计量的渐近正态性。在不可忽略缺失机制下所得的教育收益率为9.6%,符合预期。     

数据缺失机制不明确时的分布函数估计

研究了在数据缺失机制不明确时如何估计随机变量Y的分布函数FY(y),该问题不同于可以用参数模型刻画数据缺失机制时的情形,考虑到此时可能出现不可识别现象,获取一些辅助信息是必要的.借助一个可以完全观察到的随机变量X提供必须的辅助信息,构造了随机变量Y的分布函数Fy(y)的估计量,并研究了它的大样本性质.     

非随机缺失机制下基于模型的参数似然估计方法研究

在纵向抽样调查活动中,常出现变量数据缺失的情况,如何对含缺失的数据集进行总体参数估计是一个热点话题。目前已有方法主要适用于随机缺失机制下的缺失数据分析问题,常采用插补法生成完整数据集,基于此进行参数估计。本文在非随机数据缺失机制下,研究了几种基于模型的参数似然估计方法,包括模式混合模型法和选择模型法,对单调缺失模式下含缺失纵向调查数据给出了参数估计范例,进而引入随机效应参数,将两种方法加以推广。     

统计报表中异常值的识别与缺失值的预测

本文通过两个统计年报表的例子验证了统计报表中的数据近似服从对数正态分布,并利用这一特点建立了对数线性模型,对统计报表中的异常值进行识别和对缺失值作出预测。     

有缺失数据的正态母体参数的后验分布及其抽样算法

在缺失数据机制是可忽略的、先验分布是逆矩阵Γ分布的假设下,利用矩阵的cholesky分解和变量替换方法,本文导出了有单调缺失数据结构的正态分布参数的后验分布形式.进-步用后验分布的组成特点,构造了单调缺失数据结构的正态分布的协方差矩阵和均值后验分布的抽样算法.     

φ-混合样本下缺失数据情形线性模型回归系数估计的渐近性质

φ-混合样本下,当响应变量满足随机缺失机制时,利用回归填补方法填补缺失的数据,在此基础上给出了线性模型回归系数的估计,并在一定的条件下证明了估计的渐近正态性.     

缺失数据下的两步抽样法估计

在数据缺失机制形式未知时,通过两步抽样得到了分布函数的相合估计量,证明了该估计量的渐近正态性.文中假设第二次抽样时的数据缺失机制与第一次抽样时的数据缺失机制函数形式类似,允许两者有一个一维未知参数的差别.     

带有缺失数据的结构方程模型中的模型选择问题

结构方程模型在社会学、教育学、医学、市场营销学和行为学中有很广泛的应用。在这些领域中,缺失数据比较常见,很多学者提出了带有缺失数据的结构方程模型,并对此模型进行过很多研究。在这一类模型的应用中,模型选择非常重要,本文将一个基于贝叶斯准则的统计量,称为L_v测度,应用到此类模型中进行模型选择。最后,本文通过一个模拟研究及实例分析来说明L_v测度的有效性及应用,并在实例分析中给出了根据贝叶斯因子进行模型选择的结果,以此来进一步说明该测度的有效性。     

A generalization of Rado's Theorem for almost graphical boundaries

In this paper, we prove a generalization of Rado's Theorem, a fundamental result of minimal surface theory, which says that minimal surfaces over a convex domain with graphical boundaries must be disks which are themselves graphical. We will show that, for a minimal surface of any genus, whose boundary is ``almost graphical' in some sense, that the surface must be graphical once we move sufficiently far from the boundary.     

Decomposition of Covariate-Dependent Graphical Models with Categorical Data

Graphical models are wildly used to describe conditional dependence relationships among interacting random variables. Among statistical inference problems of a graphical model, one particular interest is utilizing its interaction structure to reduce model complexity. As an important approach to utilizing structural information, decomposition allows a statistical inference problem to be divided into some sub-problems with lower complexities. In this paper, to investigate decomposition of covariate-dependent graphical models, we propose some useful definitions of decomposition of covariate-dependent graphical models with categorical data in the form of contingency tables. Based on such a decomposition, a covariate-dependent graphical model can be split into some sub-models, and the maximum likelihood estimation of this model can be factorized into the maximum likelihood estimations of the sub-models. Moreover, some sufficient and necessary conditions of the proposed definitions of decomposition are studied.     

Learning the Structure of Mixed Graphical Models

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme involves a novel symmetric use of the group-lasso norm and follows naturally from a particular parameterization of the model. Supplementary materials for this article are available online.     

The Graphical Horseshoe Estimator for Inverse Covariance Matrices

We develop a new estimator of the inverse covariance matrix for high-dimensional multivariate normal data using the horseshoe prior. The proposed graphical horseshoe estimator has attractive properties compared to other popular estimators, such as the graphical lasso and the graphical smoothly clipped absolute deviation. The most prominent benefit is that when the true inverse covariance matrix is sparse, the graphical horseshoe provides estimates with small information divergence from the sampling model. The posterior mean under the graphical horseshoe prior can also be almost unbiased under certain conditions. In addition to these theoretical results, we also provide a full Gibbs sampler for implementing our estimator. MATLAB code is available for download from github at http://github.com/liyf1988/GHS. The graphical horseshoe estimator compares favorably to existing techniques in simulations and in a human gene network data analysis. Supplementary materials for this article are available online.     

An enumeration of graphical designs

Let Ψ(t,k) denote the set of pairs (v,λ) for which there exists a graphical t‐(v,k,λ) design. Most results on graphical designs have gone to show the finiteness of Ψ(t,k) when t and k satisfy certain conditions. The exact determination of Ψ(t,k) for specified t and k is a hard problem and only Ψ(2,3), Ψ(2,4), Ψ(3,4), Ψ(4,5), and Ψ(5,6) have been determined. In this article, we determine completely the sets Ψ(2,5) and Ψ(3,5). As a result, we find more than 270,000 inequivalent graphical designs, and more than 8,000 new parameter sets for which there exists a graphical design. Prior to this, graphical designs are known for only 574 parameter sets. © 2006 Wiley Periodicals, Inc. J Combin Designs 16: 70–85, 2008     

Cubic graphical regular representations of finite non-abelian simple groups

Given a finite group R, a graphical regular representation of R is a Cayley graph Γ over R with R = Aut(Γ). In this paper we study graphical regular representations of finite non-abelian simple groups of small valency.     

Graphical Representations of Speed: Obstacles Preservice K‐8 Teachers Experience

This study examines the difficulties college students experience when creating and interpreting graphs in which speed is one of the variables. Nineteen students, all preservice elementary or middle school teachers, completed an upper‐level course exploring algebraic concepts. Although all of these preservice teachers had previously completed several mathematics courses, including calculus, they demonstrated widespread misconceptions about the variable speed. This study identifies four cognitive obstacles held by the students, provides excerpts of their graphical constructions and verbal interpretations, and discusses potential causes for the confusion. In particular, misconceptions arose when students interpreted the behavior and nature of speed within a graphical context, as well as in situations where they were required to construct a graph involving speed as a variable. The study concludes by offering implications for the teaching and learning of speed and its interpretation within a graphical setting.     

Marginal and simultaneous predictive classification using stratified graphical models

An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution. Several such rules have been recently considered and their asymptotic behavior has been characterized under the assumption that the observed features or variables used for building a classifier are conditionally independent given a simultaneous labeling of both the training samples and those from an unknown origin. Here we extend the theoretical results to predictive classifiers acknowledging feature dependencies either through graphical models or sparser alternatives defined as stratified graphical models. We show through experimentation with both synthetic and real data that the predictive classifiers encoding dependencies have the potential to substantially improve classification accuracy compared with both standard discriminative classifiers and the predictive classifiers based on solely conditionally independent features. In most of our experiments stratified graphical models show an advantage over ordinary graphical models.     

Positivity for Gaussian graphical models

Gaussian graphical models are parametric statistical models for jointly normal random variables whose dependence structure is determined by a graph. In previous work, we introduced trek separation, which gives a necessary and sufficient condition in terms of the graph for when a subdeterminant is zero for all covariance matrices that belong to the Gaussian graphical model. Here we extend this result to give explicit cancellation-free formulas for the expansions of non-zero subdeterminants.     

Covariate selection for identifying the effects of a particular type of conditional plan using causal networks

Suppose that cause-effect relationships between variables can be described by a causal network with a linear structural equation model. Kuroki and Miyakawa proposed a graphical criterion for selecting covariates to identify the effect of a conditional plan with one control variable [J. Roy. Statist. Soc. Ser. B, 2003, 65: 209–222]. In this paper, we study a particular type of conditional plan with more than one control variable and propose a graphical criterion for selecting covariates to identify the effect of a conditional plan of the studied type.