正确认识和大力发展个体私营经济

改革开放以来,个体私营经济从计划经济坚硬的土地上崭露头角,显示了较强的生命力。文章试从个体私营经济存在和发展的必然性及其曲折的发展过程入手,论述了其在国民经济发展中的作用,并提出个体私营等非公有制经济发展中存在的主要问题及对策     

函数型数据的共同主成分分析探究及展望

函数型数据的主成分分析(FPCA)已经成功应用在许多领域,但它主要研究的是单样本问题.本文详细讨论了一种新近发展的函数型数据分析的理论--函数型共同主成分(CGPC)分析方法,它主要应用于检验两组函数型随机样本的分布情况.CFPC方法的理论基础是将两组函数型样本进行Karhunen-Loeve(KL)展开,并用Bootstrap方法检验两组样本的均值函数、特征值和特征函数的一致性.最后,我们对CFPC的理论研究和应用前景进行了展望.     

基于非线性SVM的上市公司财务危机预警模型研究

为了克服传统财务危机预警模型在假设前提、样本容量、泛化能力等方面的缺陷,应用非线性SVM构建了财务危机预警模型.该模型以偿债能力、营运能力、盈利能力、现金能力和成长能力等五方面的15个财务指标作为输入变量,以上市公司是否被特别处理(ST)作为输出变量,实证分析表明:该模型具有100%的训练精度和90%的验证精度,学习和预测能力良好.     

模糊条件下方案净现值的统计分析

研究模糊条件下利用模糊统计分析方法选择评价方案,首先介绍了模糊数、模糊统计分析指标的运算及其排序规则,然后根据模糊条件下方案的类型,探讨了模糊统计分析方法如何应用于方案的选择,最后利用数值案例展示了模糊统计分析方法在方案选择中的应用.该研究为模糊条件下的方案选择提供了一种理论工具.     

Automating and evaluating reversible jump MCMC proposal distributions

The reversible jump Markov chain Monte Carlo (MCMC) sampler (Green in Biometrika 82:711–732, 1995) has become an invaluable device for Bayesian practitioners. However, the primary difficulty with the sampler lies with the efficient construction of transitions between competing models of possibly differing dimensionality and interpretation. We propose the use of a marginal density estimator to construct between-model proposal distributions. This provides both a step towards black-box simulation for reversible jump samplers, and a tool to examine the utility of common between-model mapping strategies. We compare the performance of our approach to well established alternatives in both time series and mixture model examples.     

Statistical inference for partially time-varying coefficient errors-in-variables models

This paper studies the partially time-varying coefficient models where some covariates are measured with additive errors. In order to overcome the bias of the usual profile least squares estimation when measurement errors are ignored, we propose a modified profile least squares estimator of the regression parameter and construct estimators of the nonlinear coefficient function and error variance. The proposed three estimators are proved to be asymptotically normal under mild conditions. In addition, we introduce the profile likelihood ratio test and then demonstrate that it follows an asymptotically χ²${\chi }^2$ distribution under the null hypothesis. Finite sample behavior of the estimators is investigated via simulations too.     

First- and Second-order Asymptotics for the Tail Distortion Risk Measure of Extreme Risks

The tail distortion risk measure at level p was first introduced in Zhu and Li (2012 Zhu, L., Li, H. (2012). Tail distortion risk and its asymptotic analysis. Insur. Math. Econ. 51(1):115–121.[Crossref], [Web of Science ®] , [Google Scholar]), where the parameter p ∈ (0, 1) indicates the confidence level. They established first-order asymptotics for this risk measure, as p↑1, for the Fréchet case. In this article, we extend their work by establishing both first-order and second-order asymptotics for the Fréchet, Weibull, and Gumbel cases. Numerical studies are also carried out to examine the accuracy of both asymptotics.     

Composite quantile regression for varying-coefficient single-index models

ABSTRACT

The varying-coefficient single-index model (VCSIM) is a very general and flexible tool for exploring the relationship between a response variable and a set of predictors. Popular special cases include single-index models and varying-coefficient models. In order to estimate the index-coefficient and the non parametric varying-coefficients in the VCSIM, we propose a two-stage composite quantile regression estimation procedure, which integrates the local linear smoothing method and the information of quantile regressions at a number of conditional quantiles of the response variable. We establish the asymptotic properties of the proposed estimators for the index-coefficient and varying-coefficients when the error is heterogeneous. When compared with the existing mean-regression-based estimation method, our simulation results indicate that our proposed method has comparable performance for normal error and is more robust for error with outliers or heavy tail. We illustrate our methodologies with a real example.     

Semiparametric Smooth Coefficient Stochastic Frontier Model With Panel Data

ABSTRACT

We investigate the semiparametric smooth coefficient stochastic frontier model for panel data in which the distribution of the composite error term is assumed to be of known form but depends on some environmental variables. We propose multi-step estimators for the smooth coefficient functions as well as the parameters of the distribution of the composite error term and obtain their asymptotic properties. The Monte Carlo study demonstrates that the proposed estimators perform well in finite samples. We also consider an application and perform model specification test, construct confidence intervals, and estimate efficiency scores that depend on some environmental variables. The application uses a panel data on 451 large U.S. firms to explore the effects of computerization on productivity. Results show that two popular parametric models used in the stochastic frontier literature are likely to be misspecified. Compared with the parametric estimates, our semiparametric model shows a positive and larger overall effect of computer capital on the productivity. The efficiency levels, however, were not much different among the models. Supplementary materials for this article are available online.     

文化视野下的汉语民族性特点

汉语的民族性特点是在与其他民族语长期接触和融合过程中形成的.文章立足于中西文化和中华文化广阔视域,从语词类聚、句法组织、修辞方式、表达习惯等角度对汉语的民族性特点进行了全面描写,同时结合民族历史、政治制度、社会背景、价值观念、认知心理等诸因素,给予了发生学解释.