The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

点-轴系统开发模式中不同等级城市节点经济影响效应——以哈大齐工业走廊为例

点-轴理论系统在我国国土开发和区域发展中得到广泛应用并产生了深远的影响,该系统模式不同程度的体现了社会经济空间组织的有效形式,是制定区域生产力合理布局和城市重点发展战略的重要结构模式.本文以借助多元计量统计模型以及聚类分析方法来探究点轴理论系统中各个城市节点的等级,并以此挖掘不同规模等级的城市节点在整个空间区域中的经济...     

Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.     

不同动态理论视角下产业组织空间群聚机制的解释

产业组织群聚是产业组织演化的一个宏观空间特征,是一个典型的具有群聚性的网络动态过程。建立在不同的理论流派基础上对此作出了不同侧面的解释。但基于复杂系统网络演化理论基础上的解释尚很少。本文首先对产业组织群聚的不同经济流派做出比较,在此基础上,探讨网络演化的复杂性和无标度性。论证具有无标度特性的网络所内含的群聚性(Clustering)可以用来解释产业组织群聚特征,从而可扩展已有的产业组织群聚理论解释。     

信息产业发展水平综合评价体系研究

科学地分析信息产业发展水平是制定信息产业相关政策的重要基础。通过对信息产业发展阶段特征的综合分析,提出了一套科学的信息产业发展水平评价指标体系,运用层次分析法对评价指标进行综合分析,对国家信息产业发展水平作出了基本评价。通过对12个国家信息产业发展水平进行综合评价,对不同国家信息产业发展水平进行聚类分析,从而分析总结出信息产业各个发展层次的主要特征,为进一步制定信息产业发展的相关战略和政策提供了理论依据。     

基于层次聚类推理的在线品牌诊断研究及其服装系统实现

本文从服装品牌在线评估与诊断的实际需要出发,研究了面向服装品牌的在线诊断评价体系,给出了在线品牌诊断模型,提出案例推理与规则推理相结合的一种层次聚类推理方法,并根据品牌诊断流程和映射算法设计了品牌诊断推理机制,给出了一个服装在线品牌诊断系统的实现。     

数据挖掘技术在企业薪酬设针中的应用研究

现代企业薪酬设计过程中要面对数量众多、关系复杂的信息和数据,而数据挖掘技术在有效处理大量数据方面具有独特的优势。本文以薪酬设计中的数据分析过程为主线,探讨如何应用分类、聚类以及关联规则等数据挖掘技术来分析和处理数据,为薪酬设计提供强有力的决策支持。     

一种商业银行客户分割的多维方法——基于客户期望收益和态度的分割模型

客户分割与资源分配是企业一直在努力解决的问题,但目前,空前巨大的客户数据量使得准确进行市场细分和寻找目标市场变得复杂和难以有效实施。通过数据挖掘技术从大型数据库中抽取隐藏的预测信息,利用层次聚类分析建立了一个根据客户的多个态度维度进行客户分割的多维方法。结果表明,以这一方式产生的聚类在同质性较好并且通过参考人口学特征的差别能够获得客户细分市场的轮廓。此外,识别了四个有特色的、表明对信息服务和技术有特殊偏好的客户群。     

Spatial statistical modelling of insurance risk: a spatial epidemiological approach to car insurance

Spatial models, such as the Besag, York and Mollie (BYM) model, have long been used in epidemiology and disease mapping. A common research question in these subjects is modelling the number of disease events per region; here the BYM models provides a holistic framework for both covariates and dependencies between regions. We use these tools to assess the relative insurance risk associated with the policyholders geographical location. A Bayesian modelling approach is presented and an elastic net is used to reduce the large number of possible geographic covariates. The final inference is performed using Integrated Nested Laplace Approximation. The model is applied to car insurance data from If P&C Insurance together with spatially referenced covariate data of high resolution, provided by Insightone. The entire analysis is performed using freely available R-packages. Including spatial dependence when modelling the number of claims significantly improves on the result obtained using ordinary generalised linear models. However, the support for adding a spatial component to the model for claims cost is weaker.     

基于ACC-RBF的水布垭面板堆石坝参数反演分析

蚁群聚类径向基函数(ACC-RBF)神经网络是将蚁群聚类算法和径向基函数神经网络组合运用的一种新型神经网络模型,把该网络用于水布垭高面板坝堆石体的多参数反演问题,在室内试验参数的基础上用有限元计算获得学习样本,采用该网络对坝体堆石料的邓肯E-B模型参数进行反演分析,用反演所得参数结合三维非线性有限元计算坝体应力变形,并与实测资料比较,得出很接近的结论.