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.     

基于结构方程模型的市场细分方法应用研究

结构方程模型是当前研究顾客满意和顾客忠诚的主流方法之一,但基于结构方程对顾客满意和顾客忠诚进行市场细分方法的研究却十分缺乏.基于结构方程模型进行市场细分算法的应用研究,并对得出的细分结果进行了解释.     

商业模式对企业绩效的影响机理与实证研究——基于商业生态系统视角

以商业生态系统为研究视角,利用结构方程模型对企业商业模式与企业绩效之间的关系进行实证分析.主要结论有:企业商业模式的中间影响者、直接影响者和直接驱动者对企业绩效有显著影响,但商业模式的稳定影响者对企业的财务绩效影响不明显,对企业的市场绩效有显著影响.此外,企业的市场绩效对企业的财务绩效有显著的正向影响.研究结果表明:要实现企业业务价值,提升企业绩效,一是要强化商业模式各要素之间的联系,二是要增强稳定影响者匹配性,三是要关注直接影响者,夯实竞争基础.     

基于PLS-SEM的大学生求学满意度研究

在建立大学生求学满意度指数模型的基础上,运用偏最小二乘的结构方程模型对所建立的模型进行了参数求解和检验.实证研究表明:所建立的大学生求学满意度指数模型拟合度较高,并较好地刻画了各变量之间的系数.     

农业科技微信公众号用户采纳行为研究——基于信息系统成功模型与信任理论

多年来由于用户对农业科技的需求增加,农业科技微信公众号不断涌现.运用扩展信息系统成功模型和信任理论,构建结构方程模型,以对用户农业科技微信公众号使用行为进行研究.通过对168名家庭农场经营者的调查数据进行实证研究.结果表明:满意度与使用意向是实际使用的两个重要因素;满意度在服务质量、信息质量、系统质量和信任与农业科技微信公众号使用意向之间发挥着中介作用.在此研究基础上,提出一些合理的应用建议.     

基于结构方程的我国风电企业国际化竞争力实证研究

对我国风电企业国际化发展竞争力进行了模型构建和实证研究,首先基于专家访谈调研与理论综合分析的基础上,构建了包括供应商、购买者、潜在进入者、同行业竞争者、替代品和企业自身六方面的我国风电企业国际化竞争力的模型指标框架;其次,运用信效度分析与验证性因子分析等方法对所构建的国际化竞争力指标体系的结构有效性进行了检验,并根据验证结果调整模型框架;接下来利用结构方程模型得到的路径系数获得了竞争力模型的权重,构建完成风电企业国际化竞争力评价模型;最后基于构建的国际化竞争力评价模型,对我国风电案例企业以及国际风电企业维斯塔斯、西门子歌美飒进行评价,计算得出的案例企业国际化竞争力水平与当前全球风电市场竞争格局和态势基本一致.     

近年居民消费结构统计分析的研究综述——关于因子分析和聚类分析的应用

本文对近年有关居民消费结构的实证分析进行研究,简述了数据选取、实证方法的变革与演进;回顾了模型分析的过程与结论;并且进行了相应的评述、提出了建议,以期为今后统计实证手段的应用及后续研究提供相应参考。     

结构方程模型与联立方程模型的比较

本文首先介绍了结构方程模型和联立方程模型的一般形式、典型实例及其在我国的研究现状。通过将结构方程模型与联立方程模型进行比较,发现结构方程模型具有能直接处理潜变量和测量误差等优点,在管理学、心理学等社会科学中具有广泛应用;但是结构方程模型对样本量要求大、对抽样、问卷设计和调查过程的缺陷以及忽略变量等模型的设定错误不能弥补;而且,结构方程模型的参数不如联立方程模型的含义那么具体而直观。     

中国上市公司股权集中度、董事会独立性和公司绩效——基于结构方程模型的研究

股权集中度与公司绩效的关系，董事会独立性与公司绩效的关系是公司治理中的重要内容。以往的实证研究仅检验这两个直接关系。实际上公司董事会独立性，股权集中度，和公司绩效存在三角关系。本文研究此三角关系，以我国沪深300指数成分股上市公司数据为样本应用结构方程模型进行实证检验。结果发现，股权集中度与董事会独立性为负向关系，董事会独立性与公司绩效为负向关系，股权集中度对公司绩效不但有直接影响而且通过董事会独立性产生间接影响。     

c-D-Copula模型构建及其在金融风险传染中的应用

金融危机的频繁发生,使得国际金融传染的研究成为了一个重要的课题.文章在动态Copula模型的基础上,对其参数的动态演变方程进行推广,借鉴c-DCC模型的思想假定其截距项存在结构突变,构建了c-D-Copula模型.为了避免人为选择结构性变化时间,对c-D-Copula模型进行变点检测,由数据驱动自动选择断点.为了进行比较分析,同时给出了动态演变方程的参数全部变化的变结构全模型,并给出了相应的假设检验方法.文章选择美国标准普尔500指数和全球主要5个国家(地区)的股票指数的对数收益率进行实证研究,通过检验得出大多数情况下用c-D-Copula模型比变结构全模型更合适对数据进行描述.通过这5个国家或地区与美国之间联动性的动态变化,对美国次贷危机金融传染的存在性和变化过程进行了研究,变点时刻和下尾部相依系数的实证结果表明所分析的国家和地区都受到了美国次贷危机的传染.     

A machine learning approach to process improvement in a telecommunications company

Redesigning and improving business processes to better serve customer needs has become a priority in service industries as they scramble to become more competitive. This paper describes an approach to process improvement that is being developed collaboratively by applied researchers at US WEST, a major telecommunications company, and the University of Colorado. Motivated by the need to streamline and to add more quantitative power to traditional quality improvement processes, the new approach uses an artificial intelligence (AI) statistical tree growing method that uses customer survey data to identify operations areas where improvements are expected to affect customers most. This AI/statistical method also identifies realistic quantitative targets for improvement and suggests specific strategies (recommended combinations of actions) that are predicted to have high impact. This research, funded in part by the Colorado Advanced Software Institute (CASI) in an effort to stimulate profitable innovations, has resulted in a practical methodology that has been used successfully at US WEST to help set process improvement priorities and to guide resource allocation decisions throughout the company.     

The covariance structure and likelihood function for multivariate dyadic data

This paper extends the results in Li and Loken [A unified theory of statistical analysis and inference for variance component models for dyadic data, Statist. Sinica 12 (2002) 519-535] on the statistical analysis of measurements taken on dyads to the situations in which more than one attribute are measured on each dyad. Starting from the covariance structure for the univariate case obtained in Li and Loken (2002), the covariance structure for the multivariate case is derived based on the group symmetry induced by the assumed exchangeability in the units. Our primary objective is to document the Gaussian likelihood and the sufficient statistics for multivariate dyadic data in closed form, so that they can be referenced by researchers as they analyze those data. The derivation carried out can also serve as an example of multivariate extension of univariate models based on exchangeability.     

Wavelet Analysis of Big Data Contaminated by Large Noise in an fMRI Study of Neuroplasticity

Functional magnetic resonance imaging (fMRI) allows researchers to analyze brain activity on a voxel level, but using this ability is complicated by dealing with Big Data and large noise. A traditional remedy is averaging over large parts of brain in combination with more advanced technical innovations in reducing fMRI noise. In this paper a novel statistical approach, based on a wavelet analysis of standard fMRI data, is proposed and its application to an fMRI study of neuron plasticity of 24 healthy adults is presented. The aim of that study was to recognize changes in connectivity between left and right motor cortices (the neuroplasticity) after button clicking training sessions. A conventional method of the data analysis, based on averaging images, has implied that for the group of 24 participants the connectivity increased after the training. The proposed wavelet analysis suggests to analyze pathways between left and right hemispheres on a voxel-to-voxel level and for each participant via estimation of corresponding cross-correlations. This immediately necessitates statistical analysis of large-p-small-n correlation matrices contaminated by large noise. Furthermore, distributions that we are dealing in the analysis are neither Gaussian nor sub-Gaussian but sub-exponential. The paper explains how the problem may be solved and presents results of a dynamic analysis of the ability of a human brain to reorganize itself for 24 healthy adults. Results show that the ability of a brain to reorganize itself varies widely even among healthy individuals, and this observation is important for our understanding of a human brain and treatment of brain diseases.     

On the exploration of linear latent effect for multivariate modeling

A data analysis method is proposed to derive a latent structure matrix from a sample covariance matrix. The matrix can be used to explore the linear latent effect between two sets of observed variables. Procedures with which to estimate a set of dependent variables from a set of explanatory variables by using latent structure matrix are also proposed. The proposed method can assist the researchers in improving the effectiveness of the SEM models by exploring the latent structure between two sets of variables. In addition, a structure residual matrix can also be derived as a by-product of the proposed method, with which researchers can conduct experimental procedures for variables combinations and selections to build various models for hypotheses testing. These capabilities of data analysis method can improve the effectiveness of traditional SEM methods in data property characterization and models hypotheses testing. Case studies are provided to demonstrate the procedure of deriving latent structure matrix step by step, and the latent structure estimation results are quite close to the results of PLS regression. A structure coefficient index is suggested to explore the relationships among various combinations of variables and their effects on the variance of the latent structure.     

基于灰色理论的线性模型参数理论及应用

线性模型是经典统计学的基本内容,主要应用于随机数据的建模分析。如何对非随机的模糊或灰色等不分明性数据进行模型构建和统计分析。基于灰色系统理论,在一系列关于灰色统计推断理论的研究基础上,将灰色估计和灰色假设检验等方法拓展到线性模型的参数估计和假设检验中。与经典统计分析方法进行对比,为不分明数据的建模分析提供新的方法。     

基于对故障规律统计分析的汽车备件需求预测

汽车备件的需求与汽车故障紧密相关,文章介绍了一种在对汽车故障进行统计分析并确定其分布规律的基础上预测备件需求的方法,预测中需要结合整车保有量的历史数据以及故障与备件的对应表。用统计的方法对某型客车的故障信息进行分析,认为故障的规律可用四种典型的分布进行描述。实例验证了这种方法的准确性高于传统方法,并且在计算机的辅助下可以方便操作。     

Application of structural equation modeling to evaluate the intention of shippers to use Internet services in liner shipping

Operations research (OR) is the application of modeling techniques to formulate and analyze systems and problems for management decision-making. Structural equation modeling (SEM) is a modeling technique applied to social or behavioral systems to understand and explain relationships that may exist among elements of systems. Recently, the measurement of unobservable variables has gained increasing attention in operations management (OM) research, and the OR discipline has begun to recognize the value of applying SEM to analyze behavioral-related OR problems. To provide OR researchers with a better understanding of the application of this useful statistical modeling technique, this paper presents a tutorial on the application of SEM. Specifically, we investigate the key factors that affect the adoption of Internet services in the context of liner shipping services. Although [Fishbein, M.A., Ajzen, I., 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA; Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3), 319–339; Ajzen, I., 1985. From intention to actions: A theory of planned behavior. In: Kuhl, J., Bechmann, J. (Eds.), Action Control: From Cognition to Behavior. Springer Verlag, New York, pp. 11–39; Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179–211] have made important contributions to understanding users’ behavior of technology acceptance, shippers’ resistance to end-user systems is still a common problem in the liner shipping industry. To better predict, explain, and increase shippers’ acceptance of technology, we need to understand why shippers accept or reject Internet services provided by their liner shipping carriers. Another objective of this paper is to propose and empirically test a theoretical framework that relates the intention of shippers to use Internet services in liner shipping with its antecedents such as perceived usefulness, perceived ease of use, and the perceptions of security protection. Tests of the structural model confirm Davis’s (1989) notion that perceived ease of use explains the intention of shippers to use Internet services, and that perceived ease of use has a strong positive effect on perceived usefulness. The results also indicate that security protection influences perceived ease of use. The SEM analyses in this study offer OR researchers a methodological guide on how to assess the efficacy of both a measurement model that relates observed indicators to latent factors and a structural model that poses relationships between constructs.     

基于敌意风险分析方法的多阶段反恐设备选址模型

恐怖袭击一直是人类安全的重要威胁之一。随着当前恐怖袭击在全球的频发,对反恐问题的研究更加急迫。针对反恐设施选址问题,考虑资源分配的多阶段性以及动态性,根据贝叶斯决策理论和序贯博弈思想,构建了多阶段反恐设施的敌意风险分析决策模型。讨论在城市多个设施点离散选址的不同情况下,通过预防性和修复性的资源分配将恐怖袭击的损失降低到最小。以上海市区县网络为例进行编程仿真测试,结果表明,模型可给出不同给定资源设定下的最优选址方案,是一种有效并切实可行的分析方法。     

Journey Making group workshops as a research tool

Problem-structuring group workshops can be used in organizations as a consulting tool and as a research tool. One example of the latter is using a problem-structuring method (PSM) to help a group tackle an organizational issue; meanwhile, researchers collect the participants’ initial views, discussion of divergent views, the negotiated agreement, and the reasoning for outcomes emerging. Technology can help by supporting participants in freely sharing their opinions and by logging data for post-workshop analyses. For example, computers let participants share views anonymously and without being influenced by others (as well as logging those views), and video-cameras can record discussions and intra-group dynamics. This paper evaluates whether technology-supported Journey Making workshops can be effective research tools that can capture quality research data when compared against theoretical performance benchmarks and other qualitative research tools.     

Statistical Analysis of Financial Volatility by Wavelet Shrinkage

We analyze the Nikkei daily stock index and verify how wavelets can help in identifying, estimating and predicting its volatility features. While we study the conditional mean and variance dynamics, by utilizing statistical parametric inference techniques, we also decompose the observed signal with a data de-noising procedure. We thus investigate how wavelets discriminate among information at different resolution levels and we attempt to understand whether the de-noised data lead to a better identification of the underlying volatility process. We find that the wavelet data pre-processing strategy, by reducing the measurement error of the observed data, is useful for improving the volatility prediction power.