Conditional time-dependent nonparametric estimators with an application to healthcare production function

ABSTRACT

By using the probabilistic framework of production efficiency, the paper develops time-dependent conditional efficiency estimators performing a non-parametric frontier analysis. Specifically, by applying both full and quantile (robust) time-dependent conditional estimators, it models the dynamic effect of health expenditure on countries’ technological change and technological catch-up levels. The results from the application reveal that the effect of per capita health expenditure on countries’ technological change and technological catch-up is nonlinear and is subject to countries’ specific income levels.     

日本研讨自然辩证法的思想方法

日本是开展自然辩证法研究比较早的国家之一 ,并在研讨自然辩证法方面形成了许多富有启发性的思想方法。可将其概括为 :自由论争的思想方法、“具体化”的思想方法、发展的思想方法、联盟的思想方法     

Modelling heterogeneous space–time occurrences of earthquakes and its residual analysis

Summary. Earthquake intensities are modelled as a function of previous activity whose specific form is based on established empirical laws in seismology, but whose parameter values can vary from place to place. This model is used for characterizing regional features of seismic activities in and around Japan, and also for exploring regions where the actual seismicity rate systematically deviates from that of the modelled rate.     

家族企业管理机制创新探析

改革开放以来 ,我国私营经济获得了长足的发展 ,并为我国经济和社会发展做出了重要贡献 ,成为社会主义市场经济的重要组成部分。但随着市场外在条件和社会经济发展格局的变化 ,作为私营资本形式之一的家族企业 ,其自身的不适应性逐步凸现出来。克服家族式管理的不足 ,转换管理机制和管理方式 ,通过资本市场使家族企业完成由家庭化向专业化、由宗法化向市场化的转变 ,是使其真正做大做强的必由之路     

证券投资管理专业人才需求分析与人才规格设计

随着经济体制改革的深化,社会经济生活中对投融资的需求日益旺盛,大中型企业转机建制、民营企业谋求发展将更加依赖资本市场,证券业因此成为我国新兴行业,证券人才需求不仅空间巨大,而且呈现明显的层次性,既需要高层次的决策者、管理者,更需要具有较强职业技能的管理、服务第一线的应用性人才。证券投资管理专业应据此确定人才培养目标和人才培养规格。     

Goodness‐of‐fit methods for matched case‐control studies

The authors propose graphical and numerical methods for checking the adequacy of the logistic regression model for matched case‐control data. Their approach is based on the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a centered Gaussian limit whose distribution can be approximated via computer simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a certain number of simulated realizations of the approximate limiting process under the null hypothesis. The proposed techniques allow one to check the functional form of each covariate, the logistic link function as well as the overall model adequacy. The authors assess the performance of the proposed methods through simulation studies and illustrate them using data from a cardiovascular study.     

随机变量投影的一种应用

利用随机变量的投影关系,定义了偏方差矩阵,从而导出了逆方差阵元素的一种形式在随机变量是正态的条件,它为判别条件独立性有方便的操作办法。     

LS estimation of periodic autoregressive models with non-Gaussian errors: a simulation study

In this article, the least squares (LS) estimates of the parameters of periodic autoregressive (PAR) models are investigated for various distributions of error terms via Monte-Carlo simulation. Beside the Gaussian distribution, this study covers the exponential, gamma, student-t, and Cauchy distributions. The estimates are compared for various distributions via bias and MSE criterion. The effect of other factors are also examined as the non-constancy of model orders, the non-constancy of the variances of seasonal white noise, the period length, and the length of the time series. The simulation results indicate that this method is in general robust for the estimation of AR parameters with respect to the distribution of error terms and other factors. However, the estimates of those parameters were, in some cases, noticeably poor for Cauchy distribution. It is also noticed that the variances of estimates of white noise variances are highly affected by the degree of skewness of the distribution of error terms.     

Bayesian Network Structure Learning with Permutation Tests

In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e., the maximization algorithms (in score-based algorithms) or the techniques for learning the dependencies of each variable (in constraint-based algorithms). In this article, we investigate how the use of permutation tests instead of parametric ones affects the performance of Bayesian network structure learning from discrete data. Shrinkage tests are also covered to provide a broad overview of the techniques developed in current literature.     

金融资产的动态非线性相关:一个新的模型

基于Kendall’sτ秩相关系数的优越性和定义,本文提出了新的具有明确经济意义的动态条件相关copula模型,将常用的Gaussian、Clayton和Gumbel函数统一根据该演化方程实现动态化,构造出三种Kendall’sτ动态条件相关copula模型,可用于刻画不同的相关模式。这些模型不仅参数少、容易估计,避免了现有动态条件相关copula模型构建方法各异导致的在实证中不利于比较的缺点,而且能够进行多步向前预测,有效地减少了进行样本外预测时的计算量,从而为刻画时变、非线性、非对称性和尾部相关等复杂的动态相关模式提供了新方法。