因子分析精确模型的优点

本文通过因子分析精确模型的基本思想、方法、结论,与因子分析原模型作对应比较,找到了因子分析原模型和理论不完善的具体原因,得出了因子分析的精确模型和理论的优点:可以更好地替代因子分析的原模型和理论;将因子分析模型解的估计性研究转入到确定性解的研究。     

因子分析的精确模型及其解

本文从找因子分析精确解的角度,以主成份分析理论为基础,应用矩阵运算方法,建立了新的因子分析模型,消除了理论假设的误差;给出了因子分析模型的精确解;找到了因子分析与主成份分析的关系式.     

因子分析模型L及其解是更好的

本文应用因子分析模型L及其解,求出了经典因子分析模型中公因子载荷、公因子、特殊因子的精确解,解决了经典因子分析模型和理论存在的9个问题,进一步,指出了经典因子分析模型及其解根本的局限性问题：公因子解没有排除观测误差的干扰,不能达到降维的目的等。而理论和实证上,因子分析模型L及其解解决了这些问题,即因子分析模型L及其解是更好的,其为因子分析正确模型、理论和方法的使用,为因子分析法的发展建立了精确解的理论基础。同时,本文给出了因子分析法的应用建议,提出了需要进一步研究的一些相关问题。     

主成分分析与因子分析关系探讨及软件实现

文章论述了主成分分析与因子分析之间的区别与联系,同时指出SPSS软件实现主成分分析的错误之处,并给出其正确的实现方法。另外,针对文献[4,5]中提出的因子分析模型精确解加以探讨,指出其精确解实际上就是主成分解,并以具体实例加以说明。     

初始因子与旋转后因子的异同

文章运用因子分析新理论-因子分析模型L及其精确解,通过对模型、准确解、有关结论、解决问题的步骤等方面的比较,用表格和实例清晰地指出了初始因子与旋转后因子在理论和实证上的异同;明示了初始因子、旋转后因子应用的一般性条件-因子分析结构简化规则;提出了一些注意事项和有待进一步研究的问题.     

因子分析教学内容的改进

迄今国内外流行的因子分析教学内容没有优良性的结论,以至在数学上、教学上和应用上存在许多问题.为了解决这些问题,文章沿着国内外教材中因子分析教学内容的常规路径,根据近期改进的、具有优良性结论的因子分析模型L及其解,依次给出了:因子分析的新定义、特点,因子分析模型L及其解,因子何时意义明确、因子个数更好的确定方法,因子分析模型L与因子分析原模型的比较,待研究的问题等,力争在教学内容上做到思路清晰、方法简捷、理论明确、容易抓住要点,为因子分析理论与应用的教学提供一个可行的参考.     

基于因子分析和聚类分析的教学质量综合评价

本文针对目前高校教学质量测评指标多、计算量大这一问题给出了因子分析-聚类分析综合评价方法。该方法利用多元统计分析中的因子分析理论和聚类分析方法建立了教学质量综合评价模型，并利用统计分析软件SPSS实现了模型的求解。最后在现有数据的基础上，对西安邮电学院20位主讲教师的测评成绩利用因子分析和聚类分析进行实证分析，结果表明该方法简单、有效。     

主成分分析与R型因子分析的异同比较

本文从基本思想、数学模型、优缺点以及在SPSS11.0软件中的具体步骤方面,详细地综述了主成分分析与R型因子分析的异同比较,使广大的读者能够在实践应用中更加清楚主成分分析和R型因子分析,并进一步掌握主成分分析和R型因子分析如何在软件应用中的操作方法.     

因子分析教学案例的改进

迄今国内外流行的因子分析没有解的优良性结论,以至在教学案例上遗留了许多问题。为了解决这些问题,这里沿着国内外教材中因子分析教学案例的常规路径,根据近期改进的、具有优良性结论的因子分析模型L及其解等系列结论,依次给出了:因子分析模型L的综合评价步骤,SPSS软件运行命令,应用实例,注意事项等,力争在教学案例上做到思路清晰、应用便捷、容易解决实际问题,为因子分析的案例教学提供一个可行的参考。     

村域经济治理效率综合评价的非参数方法

文章从系统科学的思想出发，综合运用数据包络分析的有关理论，给出了一种用于村域经济治理效率综合评价的非参数模型及方法，讨论了模型的含义和治理有效单元与相应的多目标规划Pareto有效解之间的关系，以及治理无效单元无效的原因、调整的方向和可能达到的理想状态，通过建立在样本数据、模糊综合评判和偏序集理论上的线性规划模型对村域经济治理方案的有效性进行了分析。     

Parsimonious Gaussian mixture models

Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases. In particular, a class of eight parsimonious Gaussian mixture models which are based on the mixtures of factor analyzers model are introduced and the maximum likelihood estimates for the parameters in these models are found using an AECM algorithm. The class of models includes parsimonious models that have not previously been developed. These models are applied to the analysis of chemical and physical properties of Italian wines and the chemical properties of coffee; the models are shown to give excellent clustering performance.     

Fitting multiplicative models by robust alternating regressions

In this paper a robust approach for fitting multiplicative models is presented. Focus is on the factor analysis model, where we will estimate factor loadings and scores by a robust alternating regression algorithm. The approach is highly robust, and also works well when there are more variables than observations. The technique yields a robust biplot, depicting the interaction structure between individuals and variables. This biplot is not predetermined by outliers, which can be retrieved from the residual plot. Also provided is an accompanying robust R ²-plot to determine the appropriate number of factors. The approach is illustrated by real and artificial examples and compared with factor analysis based on robust covariance matrix estimators. The same estimation technique can fit models with both additive and multiplicative effects (FANOVA models) to two-way tables, thereby extending the median polish technique.     

Using Analysis of Variance and Factor Analysis for the Reduction of High Dimensional Variation in Time Series of Energy Consumption

Summary: In this paper the complexity of high dimensional data with cyclical variation is reduced using analysis of variance and factor analysis. It is shown that the prediction of a small number of main cyclical factors is more useful than forecasting all the time-points separately as it is usually done by seasonal time series models. To give an example for this approach we analyze the electricity demand per quarter of an hour of industrial customers in Germany. The necessity of such predictions results from the liberalization of the German electricity market in 1998 due to legal requirements of the EC in 1996.     

Hierarchical likelihood approach to non-Gaussian factor analysis

Factor models, structural equation models (SEMs) and random-effect models share the common feature that they assume latent or unobserved random variables. Factor models and SEMs allow well developed procedures for a rich class of covariance models with many parameters, while random-effect models allow well developed procedures for non-normal models including heavy-tailed distributions for responses and random effects. In this paper, we show how these two developments can be combined to result in an extremely rich class of models, which can be beneficial to both areas. A new fitting procedures for binary factor models and a robust estimation approach for continuous factor models are proposed.     

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.     

分行业的企业财务危机预警模型比较研究

世界各国学者分别用不同的统计模型对信用风险进行全行业的实证研究。中国在此方面的研究尚处起步阶段。综合运用多元判别模型、Logistic模型、主成分模型,分不同行业对企业财务危机进行预警研究。比较分析了不同行业预警模型的判别准确率,不同预警技术的判别准确率,多年度预警的可行性,预警模型的稳定性,大类、中类行业预警的通用性等问题。商业银行可以使用这些模型进行信用风险度量和信贷风险预警。     

基于公司治理信息的财务困境预警实证研究

在分别借助因子分析和单变量检验对公司财务信息和治理信息进行统计处理的基础上，构建并实证检验中国上市公司财务困境预警的两大模型，即仅包含财务信息与融合财务信息和公司治理信息的Logistic回归预警模型。实证结果表明：公司治理信息对公司陷入财务困境具有显著的影响，引入公司治理信息的模型预测能力更强。     

Bayesian Dynamic Factor Models and Portfolio Allocation

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance-matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting, and sequential portfolio allocation for a selected set of international exchange-rate-return time series. Our goals are to understand a range of modeling questions arising in using these factor models and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models and on future potential model extensions.     

中国经济总量及要素增长的基本趋势分析

研究的目的在于回答中国经济增长及生产要素的趋势到底是什么样的？其结论对中国经济的长期预测具有重要意义。虽然所研究的与现有的收敛性分析及影响因素分析不同，但从长期看，中国经济增长及生产要素有着自身的时间特性，各种影响因素是经济增长短期波动的原因。通过分析发现：中国经济（GDP）增长、资本存量服从指数时间模型，而劳动力增长则服从增长曲线时间模型。据此，并对中国GDP、资本存量及劳动力的增长速度进行了区间估计。