股骨近端防旋髓内钉治疗高龄股骨转子间骨折

因子模型在刻画因子之间的相互关系以及因子与观测变量之间的关联性具有重要作用.在实际应用中,观测数据往往呈现出时序变异多峰、偏态等特性.本文将经典的潜变量模型延伸到非齐次隐马尔可夫潜变量模型,建立了极大似然统计分析程序.经验结果展示所建立的统计程序是有效的.     

带有非齐次结构动力因子模型的估计与检验

因子模型在刻画因子之间的相互关系以及因子与观测变量之间的关联性具有重要作用.在实际应用中,观测数据往往呈现出时序变异多峰、偏态等特性.本文将经典的潜变量模型延伸到非齐次隐马尔可夫潜变量模型,建立了极大似然统计分析程序.经验结果展示所建立的统计程序是有效的.     

时序相依数据的一种基于约束Cholesky分解的简约Gauss copula建模方法

对具有时序相依性的离散数据,本文从隐变量的角度使用Gauss copula建模.不同于现有方法,本文对协方差提出一种新的约束以实现Gauss copula模型的可识别性,并基于此提出一个新的隐变量框架对隐Gauss变量的方差和协方差进行简约建模,从而将基于修正的Cholesky分解的联合建模方法推广到广义线性模型中,建立相应的理论性质.模拟和实际数据分析验证了所提出方法的性能.     

三种统计分析方法在数学建模中的应用

独立成分分析是近十年来兴起的一种新的数据处理方法.它与主成分分析,因子分析都隶属于多元统计分析方法,并且这三种方法都可以用于处理多变量大样本的数据.目前数学建模竞赛越来越受到各大高校的重视,而在数学建模中,大部分赛题都首先需要进行大样本数据的统计预处理.因此将从模型上对这三种方法进行分析与解释,并通过数学建模实例来说明这三种方法在数学建模中的应用.     

基于多元t-分布的隐马尔可夫潜变量模型的稳健推断

潜变量模型在刻画因子间的相互关系以及因子与观测变量间的关联性方面具有重要作用.在实际应用中,观测数据往往呈现出重尾和极端值等特性.将经典的潜变量模型延伸到齐次隐马尔可夫模型,并建立了基于多元t-分布的极大似然统计分析程序.经验结果展示所建立的统计程序对消除异常点的影响是有效的.     

单次多水平正交试验中效应的显著性检验

方差分析自试验设计诞生以来一直是用于分析试验中各因子是否显著的统计方法,对于正交试验设计而言其更是唯一的分析方法.然而,当正交表各列放满了被考虑的各个因子及其交互作用并且各条件组合下只能进行一次试验时,方差分析中的误差项将恒等于0,从而方差分析不再能用于对此试验设计的分析.对此,本文针对使用多水平完备正交表的单次正交试验,提出了一种新的统计分析方法.示例表明:本文提出的检验法不仅解决了方差分析无法胜任的问题,而且在表头设计有空白列从而方差分析仍能实施时,其比方差分析具有更大的局部功效.     

影响中学生数学成绩因素的通径分析

用绵阳市 6 0 0名初中学生构成样本 ,采用J¨oreskog提出的线性结构方程式模型理论 ,以及J¨oreskog和S¨orbom开发的软件系统LIREL8.3对调查的资料进行分析。结果表明 :用 2 0个显在变量和 10个隐在变量研究影响中学生数学成绩的因素 ,最后有 13个显在变量入选构成外生模型 ,有 7个隐在变量变量入选构成内生模型。Homeback、Attitud、Views、EQ、IQ和Class这 6个内生的隐在变量对中学生的数学成绩均存在不同程度的影响 ,其中以Attitud、Homeback、IQ和EQ对数学成绩的贡献最大。内生模型中 6个内生的隐在变量的R2 的平均值为 0 .2 0 ,该模型中数学成绩(Mathachi)这一变量中可预测的方差占到了 4 1% (R2 =0 .4 1) ,将这两者结合起来考虑 ,所建立的模型应当是比较有说服力的。     

带有缺失数据的纵向隐马尔可夫因子模型的贝叶斯分析

隐马尔可夫因子模型在刻画多元纵向数据的关联性和异质性具有重要作用.在实际应用中,观测数据往往呈现缺失数据.本文在纵向框架内,对缺失的数据提出了一个建模.使用一个多项模型去拟合缺失数据指标,并提出用一系列一维条件分布的联合分布来建模.每个一维条件分布不仅取决于当前变量的观测值,而且也糅合以前的观测值和丢失的信息.在贝叶斯框架内,马尔可夫链蒙特卡罗方法用于实现后验分析.带有Metropolis-Hastings算法的Gibbs采样器被用来从相关的满条件分布中抽取随机样本.后验推断基于这些模拟观测值进行展开.我们进行了模拟研究.实证结果表明,所提出的方法在模型是正确指定时是十分有效的,而且对模型偏移也具有一定的稳健性.     

基于GAM模型分析变量对产业结构的影响研究

对全国2000-2015年的产业结构以及影响变量的面板数据,使用广义加模型(GAM)研究各变量对产业结构产生的影响,分别分析了单变量和多变量对产业结构产生的影响,然后使用GAM模型对数据进行了10倍交叉验证的模拟实验.结构表明,单变量对产业结构有很大影响,其中科技金融发展指数等对产业结构呈线性作用,多变量的交互作用能够明显对产业结构产生影响,人均GDP对产业结构的影响可以通过其他因素表出.GAM模型对预测的拟合效果比较好.     

模糊广义系统的时滞依赖稳定与镇定条件

研究T-S模糊广义系统的时滞依赖稳定与镇定问题.利用Lyapunov泛函方法,得到一个线性矩阵不等式(LMIs)形式的时滞依赖稳定条件.本文所提方法考虑以前方法中通常忽略的有用的项,引入松弛变量矩阵和自由权重矩阵,估计Lyapunov泛函导数的上界;在此基础上,设计状态反馈模糊控制器,保证了闭环系统是局部正则、局部无脉冲和渐近稳定的.所得结果无需矩阵分解和利用锥补线性化方法进行迭代,最后通过两个仿真示例表明了本文结果具有较小的保守性.     

上海市农村信息化公众满意度研究

文章在定性研究和数理检验的基础上开发了农村信息化公众满意度测量量表,并建立了农村信息化公众满意度指数模型.最后,依据上海郊区农村信息化调查数据,运用结构方程模型估计技术对测评模型进行了检验和参数求解.研究表明,文章建立的农村信息化公众满意度指数模型拟合度较高;另外,通过样本数据,得到了各潜在因子之间的路径系数以及可测变量的载荷系数,并对各潜在变量之间的相关关系进行了系统分析,文章在最后就研究发现的农村信息化过程不足提出了建议.     

Linear latent variable models: the lava-package

An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.     

结构方程模型评价体系的可比性问题

随着结构方程模型评价体系的推广应用,评价体系的可比性问题值得关注。本文以顾客满意度指数(CSI)为例说明结构方程模型评价体系的可比性问题,并通过模拟研究说明结构方程模型评价体系保持可比性的关键,那就是在其他条件一致的情况下,各结构方程模型中对核心变量的显变量设定一致,而各结构方程模型纳入何种潜变量以及如何设定潜变量之间关系,对评价体系可比性的影响并不显著。     

Regional development assessment: A structural equation approach

We propose a multivariate statistical framework for regional development assessment based on structural equation modelling with latent variables and show how such methods can be combined with non-parametric classification methods such as cluster analysis to obtain development grouping of territorial units. This approach is advantageous over the current approaches in the literature in that it takes account of distributional issues such as departures from normality in turn enabling application of more powerful inferential techniques; it enables modelling of structural relationships among latent development dimensions and subsequently formal statistical testing of model specification and testing of various hypothesis on the estimated parameters; it allows for complex structure of the factor loadings in the measurement models for the latent variables which can also be formally tested in the confirmatory framework; and enables computation of latent variable scores that take into account structural or causal relationships among latent variables and complex structure of the factor loadings in the measurement models. We apply these methods to regional development classification of Slovenia and Croatia.     

Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models

Non-linear structural equation models are widely used to analyze the relationships among outcomes and latent variables in modern educational, medical, social and psychological studies. However, the existing theories and methods for analyzing non-linear structural equation models focus on the assumptions of outcomes from an exponential family, and hence can’t be used to analyze non-exponential family outcomes. In this paper, a Bayesian method is developed to analyze non-linear structural equation models in which the manifest variables are from a reproductive dispersion model (RDM) and/or may be missing with non-ignorable missingness mechanism. The non-ignorable missingness mechanism is specified by a logistic regression model. A hybrid algorithm combining the Gibbs sampler and the Metropolis–Hastings algorithm is used to obtain the joint Bayesian estimates of structural parameters, latent variables and parameters in the logistic regression model, and a procedure calculating the Bayes factor for model comparison is given via path sampling. A goodness-of-fit statistic is proposed to assess the plausibility of the posited model. A simulation study and a real example are presented to illustrate the newly developed Bayesian methodologies.     

Shifted Legendre Polynomials algorithm used for the dynamic analysis of viscoelastic pipes conveying fluid with variable fractional order model

The dynamic analysis of viscoelastic pipes conveying fluid is investigated by the variable fractional order model in this article. The nonlinear variable fractional order integral-differential equation is established by introducing the model into the governing equation. Then the Shifted Legendre Polynomials algorithm is first presented for dealing with this kind of equations. The convergence analysis and numerical example verify that the algorithm is an effective and accurate technique for addressing this type complicated equation. Numerical results for dynamic analysis of viscoelastic pipes conveying fluid show the effect of parameters on displacement, acceleration, strain and stress. It also indicates that how dynamic properties are affected by the variable fractional order and fluid velocity varying. Most of all, the proposed algorithm has enormous potentials for the problem of high precision dynamics under the variable fractional order model.     

Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities

Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between factors. The capability of SEM in empirical validation combined with the prediction and diagnosis capabilities of Bayesian modeling facilitates effective decision making from identification of causal relationships to decision support. This study applies the proposed integrated approach to decision support for customer retention in a virtual community. The application results provide insights for practitioners on how to retain their customers. This research benefits Bayesian researchers by providing the application of modeling causal relationships at latent variable level, and helps SEM researchers in extending their models for managerial prediction and diagnosis.     

Micro–macro multilevel latent class models with multiple discrete individual-level variables

An existing micro–macro method for a single individual-level variable is extended to the multivariate situation by presenting two multilevel latent class models in which multiple discrete individual-level variables are used to explain a group-level outcome. As in the univariate case, the individual-level data are summarized at the group-level by constructing a discrete latent variable at the group level and this group-level latent variable is used as a predictor for the group-level outcome. In the first extension, that is referred to as the Direct model, the multiple individual-level variables are directly used as indicators for the group-level latent variable. In the second extension, referred to as the Indirect model, the multiple individual-level variables are used to construct an individual-level latent variable that is used as an indicator for the group-level latent variable. This implies that the individual-level variables are used indirectly at the group-level. The within- and between components of the (co)varn the individual-level variables are independent in the Direct model, but dependent in the Indirect model. Both models are discussed and illustrated with an empirical data example.     

Inferring social structure from continuous‐time interaction data

Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous‐time methods for modeling such data are based on point processes and directly model interaction “contagion,” whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal‐relational point process models for continuous‐time event data. We characterize interactions between a pair of actors as either spurious or as resulting from an underlying, persistent connection in a latent social network. We argue that consistent deviations from expected behavior, rather than solely high frequency counts, are crucial for identifying well‐established underlying social relationships. This study aims to explore these latent network structures in two contexts: one comprising of college students and another involving barn swallows.     

城镇社区居民参与体育健身行为结构模型与应用

通过国内外相关文献分析及定性研究确定构成城镇社区居民体育健身行为的可能影响因子,运用结构方程模型方法,建立了城镇社区居民参与体育健身行为结构概念模型,提出了5个假设.并与实践结合,对模型进行实证分析和验证,同时,借助于结构方程专用软件(Lisrel),对提出的概念模型加以拟合,确定了各要素之间的路径系数,验证了5个假设.最后指出了健身行为模型计算中存在的问题以及在未来研究中的应用前景.