非对称信息、信贷配给与中小企业间接融资缺口的经济学分析

中小企业在我国经济建设中起着不可低估的作用,为我国的经济发展做出了巨大的贡献。然而,由于中小企业和银行之间存在着信息的非对称性,从而导致银行在信贷市场上面临着严重的道德风险和逆向选择。银行由此对中小企业实施信贷配给,致使中小企业融资难,通过建立中小企业间接融资缺口模型来证实这一问题,并由此做出相应的经济学分析。     

灾后新生旅游地居民的生活质量满意度属性研究——以汶川映秀镇和北川永昌镇为例

灾害旅游是挖掘灾害遗产资源、加快灾后复兴的有效途径。居民生活质量满意度是确保灾害旅游重建可持续发展的关键。以灾后新生旅游地汶川映秀镇和北川永昌镇为案例,从非对称影响视角入手,应用Tetra-class模型对灾后新生旅游地居民生活质量满意度属性进行研究,结果显示：生活质量属性对居民总体满意度的影响模式涵盖Tetra-class模型的四种类型;生活质量属性绩效评价为居民总体满意度提升提供有针对性的改进策略;生活质量满意度结构模型不仅包括生活质量属性绩效与总体满意度的关系,还揭示了各属性所对应的居民生活需求层次以及灾后美好生活政策供给层面,能够为特定情境化生活质量满意度评估提供一般化视角。     

基于PLS路径模型的顾客满意度测评研究

根据美国顾客满意度指数模型,提出了一种基于PLS路径模型的顾客满意度测评模型;详细阐述了模型设定、模型估计和模型评价。实例证明该顾客满意度测评模型显变量符合单一纬度条件、具有良好的内敛效度、具有一定的解释能力,拟合效果可以接受,为国内顾客满意度测评研究提供了实用方法。     

中介机构参与下联盟企业知识转移的三方利益博弈分析

知识转移是企业保存知识、提升创新能力的重要途径,中介机构在转移过程中发挥了重要作用。本文以联盟企业间知识转移为研究对象,考虑了企业的不同心理压力,首先建立了联盟企业双方的讨价还价博弈模型;其次构建了考虑中介机构参与知识转移的博弈模型,分析和讨论三方收益变化的影响因素及策略选择。结果表明,在直接知识转移博弈中,转移主体的收益大小与自身的心理压力呈负相关,而与对方的心理压力呈正相关,同等心理压力下知识转出方占优;中介机构参与博弈情形下,中介机构作为协作角色的博弈收益仅受博弈双方心理压力影响,且与知识接收方协作可获得更大收益,而主导角色博弈情形下收益受到三方主体心理压力的综合影响,并在主导知识接收博弈时获得收益更大;中介机构的角色选择与知识转出方心理压力变化相关性不明显,主要受到知识接收方及自身心理压力影响。     

信息不对称下价格随机的应急数量折扣契约研究

以市场需求和市场价格随机波动的二级供应链为研究对象,寻找其在信息不对称条件下绩效优化的路径。通过显示原理分别构建生产成本和销售成本信息不对称时应急供应链的数量折扣契约模型,得到最优订货量与最佳批发价策略。通过具体的算例加以验证,分析了信息不对称程度对供应链上成员及整个供应链绩效的影响。研究结果表明：当突发事件造成市场价格随机波动和市场需求变化时,供应链上的跟随者能从隐瞒私人信息中获利。但当市场需求增大时,整个供应链绩效受损,反之则反是。这与以往研究在市场需求随机、市场价格固定和信息不对称条件下的结论迥异。     

On distribution-weighted partial least squares with diverging number of highly correlated predictors

Summary.  Because highly correlated data arise from many scientific fields, we investigate parameter estimation in a semiparametric regression model with diverging number of predictors that are highly correlated. For this, we first develop a distribution-weighted least squares estimator that can recover directions in the central subspace, then use the distribution-weighted least squares estimator as a seed vector and project it onto a Krylov space by partial least squares to avoid computing the inverse of the covariance of predictors. Thus, distrbution-weighted partial least squares can handle the cases with high dimensional and highly correlated predictors. Furthermore, we also suggest an iterative algorithm for obtaining a better initial value before implementing partial least squares. For theoretical investigation, we obtain strong consistency and asymptotic normality when the dimension p of predictors is of convergence rate O { n ^1/2/ log ( n )} and o ( n ^1/3) respectively where n is the sample size. When there are no other constraints on the covariance of predictors, the rates n ^1/2 and n ^1/3 are optimal. We also propose a Bayesian information criterion type of criterion to estimate the dimension of the Krylov space in the partial least squares procedure. Illustrative examples with a real data set and comprehensive simulations demonstrate that the method is robust to non-ellipticity and works well even in 'small n –large p ' problems.     

The additive hazards model with high-dimensional regressors

This paper considers estimation and prediction in the Aalen additive hazards model in the case where the covariate vector is high-dimensional such as gene expression measurements. Some form of dimension reduction of the covariate space is needed to obtain useful statistical analyses. We study the partial least squares regression method. It turns out that it is naturally adapted to this setting via the so-called Krylov sequence. The resulting PLS estimator is shown to be consistent provided that the number of terms included is taken to be equal to the number of relevant components in the regression model. A standard PLS algorithm can also be constructed, but it turns out that the resulting predictor can only be related to the original covariates via time-dependent coefficients. The methods are applied to a breast cancer data set with gene expression recordings and to the well known primary biliary cirrhosis clinical data.     

Models for the analysis of C-reactive protein in statin trials

Elevation in C-reactive protein (CRP) is an independent risk factor for cardiovascular disease progression and levels are reduced by treatment with statins. However, on-treatment CRP, given baseline CRP and treatment, is not normally distributed and outliers exist even when transformations are applied. Although classical non-parametric tests address some of these issues, they do not enable straightforward inclusion of covariate information. The aims of this study were to produce a model that improved efficiency and accuracy of analysis of CRP data. Estimation of treatment effects and identification of outliers were addressed using controlled trials of rosuvastatin. The robust statistical technique of MM-estimation was used to fit models to data in the presence of outliers and was compared with least-squares estimation. To develop the model, appropriate transformations of the response and baseline variables were selected. The model was used to investigate how on-treatment CRP related to baseline CRP and estimated treatment effects with rosuvastatin. On comparing least-squares and MM-estimation, MM-estimation was superior to least-squares estimation in that parameter estimates were more efficient and outliers were clearly identified. Relative reductions in CRP were higher at higher baseline CRP levels. There was also evidence of a dose-response relationship between CRP reductions from baseline and rosuvastatin. Several large outliers were identified, although there did not appear to be any relationships between the incidence of outliers and treatments. In conclusion, using robust estimation to model CRP data is superior to least-squares estimation and non-parametric tests in terms of efficiency, outlier identification and the ability to include covariate information.     

A robust QTL mapping procedure

In quantitative trait linkage studies using experimental crosses, the conventional normal location-shift model or other parameterizations may be unnecessarily restrictive. We generalize the mapping problem to a genuine nonparametric setup and provide a robust estimation procedure for the situation where the underlying phenotype distributions are completely unspecified. Classical Wilcoxon–Mann–Whitney statistics are employed for point and interval estimation of QTL positions and effects.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.