Bayesian Evaluation of Inequality-Constrained Hypotheses in SEM Models Using Mplus

Researchers in the behavioral and social sciences often have expectations that can be expressed in the form of inequality constraints among the parameters of a structural equation model resulting in an informative hypothesis. The questions they would like an answer to are “Is the hypothesis Correct” or “Is the hypothesis incorrect?” We demonstrate a Bayesian approach to compare an inequality-constrained hypothesis with its complement in an SEM framework. The method is introduced and its utility is illustrated by means of an example. Furthermore, the influence of the specification of the prior distribution is examined. Finally, it is shown how the approach proposed can be implemented using Mplus.     

A Nonlinear Structural Equation Mixture Modeling Approach for Nonnormally Distributed Latent Predictor Variables

Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we describe a nonlinear structural equation mixture approach that integrates the strength of parametric approaches (specification of the nonlinear functional relationship) and the flexibility of semiparametric structural equation mixture approaches for approximating the nonnormality of latent predictor variables. In a comparative simulation study, the advantages of the proposed mixture procedure over contemporary approaches [Latent Moderated Structural Equations approach (LMS) and the extended unconstrained approach] are shown for varying degrees of skewness of the latent predictor variables. Whereas the conventional approaches show either biased parameter estimates or standard errors of the nonlinear effects, the proposed mixture approach provides unbiased estimates and standard errors. We present an empirical example from educational research. Guidelines for applications of the approaches and limitations are discussed.     

有随机约束的混合回归模型的一类有偏估计

提出了有随机约束的混合回归模型在椭球约束下的一类有偏估计,并给出了在均方误差矩阵和PC准则下此估计类优于一般线性模型在椭球约束下的有偏估计类的条件.     

智能手机短信过滤方法的研究

介绍了智能手机短信过滤器的基本功能,以及实现短信过滤的关键技术,给出了过滤器设计流程和实现方法。     

暗标拍卖的博弈分析

对于拍卖问题的理论研究始于哥伦比亚大学的William Vicrey教授的开创性研究.在此之后,相关理论也开始展开,并结合了博弈论的理论知识.作为拍卖理论中的一个分支,暗标拍卖是指参与拍卖的竞价者在互不知道其他竞价者的报价情况下各自出价,统一时间开标,价高者中标的拍卖行为.在博弈论的理论中,暗标拍卖实际上是一个不完全信息博弈,即静态贝叶斯博弈.通过建立暗标拍卖的策略模型,并进行详细数理推导,得出在参与竞价的人数为n的情况下的最佳博弈方i的最佳报价策略.     

多维正态分布参数估计的损失函数和风险函数的Bayes估计

给出了多维正态分布在NP(θ,∑)和方差∑已知情况下多维参数均值θ=(θ1,θ2,…,θP)估计的损失函数和风险函数的Bayes估计.     

基于粗糙集的多目标类Bayes粗糙集模型

介绍了两个目标类的决策信息系统中各目标类的Bayes粗糙集模型,并将这一模型推广到具有多目标类的情形.最后讨论了该模型的相关性质,计算实例表明该模型是有效的.     

Bayes线性无偏估计的稳健性

基于错误的先验假定下获得了线性模型下可估函数的Bayes线性无偏估计(BLUE),证明了在均方误差矩阵(HSEH)准则下BLUE相对于广义最小二乘估计(GLSE)的优良性,并导出了它们的相对效率的界,从而获得BLUE的稳健性.     

A new feature selection based on comprehensive measurement both in inter-category and intra-category for text categorization

The feature selection, which can reduce the dimensionality of vector space without sacrificing the performance of the classifier, is widely used in text categorization. In this paper, we proposed a new feature selection algorithm, named CMFS, which comprehensively measures the significance of a term both in inter-category and intra-category. We evaluated CMFS on three benchmark document collections, 20-Newsgroups, Reuters-21578 and WebKB, using two classification algorithms, Naïve Bayes (NB) and Support Vector Machines (SVMs). The experimental results, comparing CMFS with six well-known feature selection algorithms, show that the proposed method CMFS is significantly superior to Information Gain (IG), Chi statistic (CHI), Document Frequency (DF), Orthogonal Centroid Feature Selection (OCFS) and DIA association factor (DIA) when Naïve Bayes classifier is used and significantly outperforms IG, DF, OCFS and DIA when Support Vector Machines are used.     

一种针对已知作者的姓名消歧方法

在外文期刊数据库中,同一姓名简称代表多位作者的现象十分普遍,严重影响作者检索的精度。本次研究将规则与算法相结合,依据规则为分类算法标注训练数据,从而在无监督条件下使用有监督算法,实现作者的精确检索。该算法适用于论文查证等已知作者身份的姓名消歧问题,相比通用的消歧方法,该方法结合无监督算法无需人工标注的优点,以及有监督算法高效率、易对应实体的优点。实践结果表明,该方法具有较高的准确度。