Choosing Principal Components: A New Graphical Method Based on Bayesian Model Selection

This article approaches the problem of selecting significant principal components from a Bayesian model selection perspective. The resulting Bayes rule provides a simple graphical technique that can be used instead of (or together with) the popular scree plot to determine the number of significant components to retain. We study the theoretical properties of the new method and show, by examples and simulation, that it provides more clear-cut answers than the scree plot in many interesting situations.     

我国企业青年志愿者与志愿服务研究

企业志愿者群体的诞生与壮大是中国公益事业近十年发展的一项特别成就。目前,国内对企业志愿者群体的理解和认识,与他们对建设公民社会的价值和潜力相比还远远不够。企业志愿者已成为中国公益事业的新生力量,其中,青年志愿者占大多数。对企业员工参与志愿服务活动的动机、影响因素以及如何评估企业志愿服务,进行全面的梳理,发现不论是在企业志愿者的定义、特征上,还是动机、影响因素、评估研究上,都有很大的拓展空间。     

基于全局因子分析的地区经济发展潜力研究

本文通过构建地区经济发展潜力的多指标体系,运用全局因子分析法对我国各省的经济发展潜力作出了综合评价,在此基础上,通过系统聚类分析将各省市划分为四类地区,并对各类地区的经济发展潜力的增长趋势进行了研究和探讨,分析结果表明,随着时间的推移,经济发展潜力突出的地方将更具竞争力,从而,地区间的经济差距将日益扩大。     

Bias of factor loadings from questionnaire data with imputed scores

This study investigated the bias of factor loadings obtained from incomplete questionnaire data with imputed scores. Three models were used to generate discrete ordered rating scale data typical of questionnaires, also known as Likert data. These methods were the multidimensional polytomous latent trait model, a normal ogive item response theory model, and the discretized normal model. Incomplete data due to nonresponse were simulated using either missing completely at random or not missing at random mechanisms. Subsequently, for each incomplete data matrix, four imputation methods were applied for imputing item scores. Based on a completely crossed six-factor design, it was concluded that in general, bias was small for all data simulation methods and all imputation methods, and under all nonresponse mechanisms. Imputation method, two-way-plus-error, had the smallest bias in the factor loadings. Bias based on the discretized normal model was greater than that based on the other two models.     

大学生主观生活质量及其影响因素研究

大学生的主观生活质量是目前学术领域研究比较薄弱的一个环节。文章通过实证调查所得资料的分析，认为当今大学生的主观生活质量总体上处于比较理想的水平，并通过因子分析和多元回归等统计方法，发现影响大学生主观生活质量的因素首先是学习满意度，其次包括人际关系满意度、精神满意度等10个方面。性别、家庭经济状况和年级对大学生的主观生活质量也有不同程度的影响。     

品牌形象对购买意愿影响的性别差异研究——以自用化妆品市场为例

本文以自用化妆品消费者为研究对象,通过调查问卷收集数据并利用因子分析和Logistic回归的方法对数据进行分析处理,由此探讨了品牌形象对消费者购买意愿影响的性别差异。研究结果表明,厂商或企业为了更好地提高其顾客的购买意愿,要区别对待不同性别的消费者,做到有目的、有计划、有组织地建立其产品的品牌形象,从而使企业或厂商在竞争中立于不败之地。     

中国省际建设用地单要素效率评价与区域差异研究

以单要素效率理论作基础,用1999-2008年全国分省数据,测度不同省份建设用地单要素效率,且对单要素效率的空间变化进行分析.结果表明2008年全国各省建设用地单要素效率平均值是0.5009,总体水平不高;从东部至西部,建设用地单要素效率递减;1999-2008年间东中西三大地区建设用地单要素效率均处在上升态势,上升幅度分别是:0.0405、0.0298和0.0253.研究认为1999 -2008年我国建设用地供给调控政策无论在东部还是中西部皆取得一定绩效,但东部地区调控效果显著好于中西部;应以建设用地单要素效率作依据,结合不同地区资源禀赋状况、经济发展阶段和区域功能定位实施差别化的建设用地调控政策.     

Several biplot methods applied to gene expression data

DNA microarray experiments result in enormous amount of data, which need careful interpretation. Biplot approaches show simultaneous display of genes and samples in low-dimensional graphs and thus can be used to represent the relationships between genes and samples. There are several different types of biplots, and these methods need to be evaluated because each plot provides different result.     

劳动力流动、资本转移和生产率增长——对中国工业“结构红利假说”的实证检验

 要素流动是影响生产率增长的一个重要原因，两者的关系反映在“结构红利假说”中：当投入要素从低生产率或者低生产率增长的部门向高生产率或高生产率增长部门流动时，会促进总生产率增长。本文使用常用的和扩展的shift-share方法实证检验了1998-2004年期间中国省际工业间的劳动力流动和资本转移对生产率增长的影响，发现劳动力流动对生产率增长的促进作用不显著；资本转移对生产率增长具有一定的促进作用；考虑Verdoorn效应后，劳动力和资本转移对中国工业TFP增长的总贡献较小，即结构红利假说并不显著；中国工业生产率增长的绝大部分原因还是内部增长效应。因此，更合理的配置省际工业间的生产要素显得很有必要。     

DYNAMIC FACTOR MODELS

This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated simulators. This class of factor models can represent processes with time varying conditional mean, variance, skewness and excess kurtosis. Applications discussed in the paper include dynamic risk analysis, such as risk in price variations (models with stochastic mean and volatility), extreme risks (models with stochastic tails), risk on asset liquidity (stochastic volatility duration models), and moral hazard in insurance analysis.

We propose estimation procedures for models with the marginal density of the series and factor dynamics parameterized by distinct subsets of parameters. Such a partitioning of the parameter vector found in many applications allows to simplify considerably statistical inference. We develop a two- stage Maximum Likelihood method, called the Finite Memory Maximum Likelihood, which is easy to implement in the presence of multiple factors. We also discuss simulation based estimation, testing, prediction and filtering.