欧元长期低迷的外在制约因素分析

现在美国的经济增长速度开始放缓 ,而欧盟的经济状况在经历两年的调整之后进入一个比较稳定的发展阶段。从理论上来讲欧元的汇率应该有一个坚挺的趋势 ,但事实却相反 ,欧元却一直走弱。本文通过欧盟和美国对欧元态度的对比 ,对这种现象做了一些分析。美国出于自身的利益考虑不希望欧元走强 ;而欧盟各国也都乐于看到欧元疲软 ;与此同时市场投资者也对欧元信心不足。这些态度其实成了解释欧元汇率的走势为什么屡屡出乎预测家的估计 ,一直保持相对弱势的关键原因     

论我国企业编制"增值表"的现实必要性

增值表确能提供相当有用的财务信息 ,它丰富了企业对外报表的内容 ,借鉴国外有关社会责任会计的理论和方法 ,编制有中国特色的增值表 ,不仅必要 ,而且势在必行     

我国货币政策失效的原因分析及对策

1997年以来,我国的货币政策在经济增长中起到了一定的作用,但也存在一些失效,主要表现在利率机制、准备金政策、公开市场业务等方面存在缺陷。因此,必须不断完善我国的货币政策,以便更好地为经济增长提供政策指导。     

中国劳动力参与率方面存在的问题

目前我国劳动力参与率方面仍存在的许多问题,如劳动力参与率水平总体较高,其中的女性人口劳动力参与率、青年人口劳动力参与率和老年人口劳动力参与率均尤为高。针对这种情况,可考虑通过大力发展教育事业,提高法定劳动年龄界限来解决。     

Bonus—Malus模型在银行贷款机制中的应用

将Bonus—MalLis模型应用到银行贷款业务，通过调整银行的Bonus-MalLis贷款利率来减少贷款欺诈行为。主要是建立借款人的银行个人信用体系，通过借款人上阶段的还款利率和表现决定其下阶段还款利率，这样就提供了一种与完全审计机制不同的奖惩机制。在一些简单假设下可证明Bonus—Malus利率将会消除所有欺诈行为，而非仅仅减少欺诈行为。     

心律波动信号的1/f噪声特性分析

心律波动信号具有1/f噪声特性,是非平稳的。该文采用平均功率谱密度方法,分析了基于平均功率谱密度和最小二乘拟合法求正常人和心脏充血患者的心律波动信号的谱参数,发现根据谱参数可以定量地区分正常人与心脏充血患者:大部分正常人的心律波动信号的谱参数大于1,而大部分心脏充血患者的心律波动信号的谱参数小于1,该结论可为心脏病的诊断与预诊断提供一种辅助手段。     

BATHTUB-TYPE CURVES IN RELIABILITY AND BEYOND

Finding optimal, or at least good, maintenance and repair policies is crucial in reliability engineering. Likewise, describing life phases of human mortality is important when determining social policy or insurance premiums. In these tasks, one searches for distributions to fit data and then makes inferences about the population(s). In the present paper, we focus on bathtub‐type distributions and provide a view of certain problems, methods and solutions, and a few challenges, that can be encountered in reliability engineering, survival analysis, demography and actuarial science.     

On Rates of Convergence for Bayesian Density Estimation

Abstract.  We consider the problem of estimating a compactly supported density taking a Bayesian nonparametric approach. We define a Dirichlet mixture prior that, while selecting piecewise constant densities, has full support on the Hellinger metric space of all commonly dominated probability measures on a known bounded interval. We derive pointwise rates of convergence for the posterior expected density by studying the speed at which the posterior mass accumulates on shrinking Hellinger neighbourhoods of the sampling density. If the data are sampled from a strictly positive, α -Hölderian density, with α  ∈ ( 0,1] , then the optimal convergence rate n^{− α / (2 α +1)} is obtained up to a logarithmic factor. Smoothing histograms by polygons, a continuous piecewise linear estimator is obtained that for twice continuously differentiable, strictly positive densities satisfying boundary conditions attains a rate comparable up to a logarithmic factor to the convergence rate n ^−4/5 for integrated mean squared error of kernel type density estimators.     

On an adaptive version of the Metropolis–Hastings algorithm with independent proposal distribution

In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π.     

Optimal scaling of Metropolis algorithms: Heading toward general target distributions

The authors provide an overview of optimal scaling results for the Metropolis algorithm with Gaussian proposal distribution. They address in more depth the case of high‐dimensional target distributions formed of independent, but not identically distributed components. They attempt to give an intuitive explanation as to why the well‐known optimal acceptance rate of 0.234 is not always suitable. They show how to find the asymptotically optimal acceptance rate when needed, and they explain why it is sometimes necessary to turn to inhomogeneous proposal distributions. Their results are illustrated with a simple example.