商业信用风险管理绩效影响因素的实证分析

近年来,Jianmin Jia等学者对"我国信用风险管理现状"及其相关问题进行研究,取得一定的研究成果,但相应研究中存在着"简单使用线性回归,未能消除变量间的相互影响"等局限。基于对已有相关研究成果的评析,并运用因子分析和Pearson相关分析方法,对我国商业信用风险管理绩效的影响因素进行实证研究。研究表明:商业信用风险管理绩效的关键影响因素为"信用管理职能设置"、"客户资信评估"和"信用政策制定",我国企业应从"完善信用风险管理职能"、"改善信息不对称状况"和"提高信用政策制定水平"三方面提高风险管理绩效。     

输入模态对认知负荷及听力成绩的影响

探讨了输入模态对学生认知负荷和听力理解的影响。90名来自两个平行班的大学英语学生被平均分为三个小组参加实验:文本输入组,声音输入组,文本和声音输入组。文本组的听力题目用文本呈现,声音组的听力题目仅用声音呈现,文本和声音组的题目则是采用文本和声音双重模态呈现。认知负荷量表结果和听力测试结果显示不同模态的听力输入形式对听力成绩有显著影响:文本组学生的成绩最高,其次是文本加音频组,音频输入组的成绩最低。认知负荷量表结果显示文本组的认知负荷最低,然后是文本加音频组,音频输入组的认知负荷最高。认知负荷与听力成绩的相关分析显示负荷程度与听力成绩呈负相关。研究结果对听力教学有较好的借鉴作用。     

浅谈矩阵特征值的估计

矩阵的特征值问题是矩阵计算的一个重要方向,在众多的领域中都得到了应用。在这样的大背景下,有必要深入地研究矩阵的特征值的估计问题。基于此,本文将结合国内外对于矩阵的特征值的估计方面的研究,以及一些具体的应用实例,来深入地探索矩阵的特征值的估计问题。     

Asymptotic distribution of regression correlation coefficient for Poisson regression model

This study treats an asymptotic distribution for measures of predictive power for generalized linear models (GLMs). We focus on the regression correlation coefficient (RCC) that is one of the measures of predictive power. The RCC, proposed by Zheng and Agresti is a population value and a generalization of the population value for the coefficient of determination. Therefore, the RCC is easy to interpret and familiar. Recently, Takahashi and Kurosawa provided an explicit form of the RCC and proposed a new RCC estimator for a Poisson regression model. They also showed the validity of the new estimator compared with other estimators. This study discusses the new statistical properties of the RCC for the Poisson regression model. Furthermore, we show an asymptotic normality of the RCC estimator.     

Testing for heteroscedasticity of exponential correlation mixed-effects linear models based on M-estimation

Homogeneity of variance is a basic assumption in longitudinal data analysis. However, the assumption is not necessarily appropriate. In this paper, Fisher scoring method is applied to get M-estimator in the exponential correlation mixed-effects linear model. The score tests for heteroscedasticity and correlation coefficient based on M-estimator are then studied. Monte Carlo method is applied to investigate the properties of test statistics. At last, the methods and properties are illustrated by an actual data example.     

Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes

We consider spatial point processes with a pair correlation function, which depends only on the lag vector between a pair of points. Our interest is in statistical models with a special kind of ‘structured’ anisotropy: the pair correlation function is geometric anisotropic if it is elliptical but not spherical. In particular, we study Cox process models with an elliptical pair correlation function, including shot noise Cox processes and log Gaussian Cox processes, and we develop estimation procedures using summary statistics and Bayesian methods. Our methodology is illustrated on real and synthetic datasets of spatial point patterns.     

On a characterization of the dispersion matrix based on the properties of regression

This paper provides a partial solution to a problem posed by J. Neyman (1965) regarding the characterization of multivariate negative binomial distribution based on the properties of regression. It is shown that some of the properties of regression characterize the form of the nonsingular dispersion matrix of the parent distribution, which, interestingly enough, corresponds to only two types viz. those of positive and negative multivariate binomial distributions.     

Skew-normal alpha-power model

In this paper, authors study properties and inference for the newly introduced skew-normal alpha-power model, generalizing both, the power-normal and skew-normal models. Inference is approached via maximum likelihood. Fisher information matrix is derived and shown to be nonsingular at the whole parametric space. Special emphasis is placed on the special case of the power–skew-normal model. Studies with real data illustrate the fact that the model can be very useful in applications, being able to overfit less general models entertained in the literature.     

Causality and interdependence analysis in linear econometric models with an application to fertility

This paper is an applied analysis of the causal structure of linear multi-equational econometric models. Its aim is to identify the kind of relationships linking the endogenous variables of the model, distinguishing between causal links and feedback loops. The investigation is first carried out within a deterministic framework and then moves on to show how the results may change inside a more realistic stochastic context. The causal analysis is then specifically applied to a linear simultaneous equation model explaining fertility rates. The analysis is carried out by means of a specific RATS programming code designed to show the specific nature of the relationships within the model.     

Performance of traditional f tests in repeated measures designs under covariance heterogeneity

This study investigates the performance of two traditional F tests, one for main effects and the other for interaction in repeated measures designs under several conditions of covariance heterogeneity. Overall, the test for interaction is more vulnerable than the one for main effects. Distortion in the level of significance is less serious for the case of equal group size.