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。     

冠状动脉收缩期受压持续到舒张期的模拟实验分析

利用研制的心肌桥模拟装置,对冠状动脉受压迫状态建立了圆形收缩的实验模型,进行了冠状动脉受心肌桥压迫持续到舒张期情况下的血压和流量模拟实验.实验研究表明,冠状动脉面积收缩接近到原来的20%时,远段收缩压迅速与近段收缩压分离;当冠状动脉面积收缩到小于原来的20%时,近段收缩压迅速上升,远段压力从不稳定状态迅速过渡到收缩压呈反向分布状态.心率和近段血压范围不变时,流量随着收缩程度的增加而增加.     

经济全球化作用下国际资本流动的动力机制研究

经济全球化的深入发展以及与之相关的信息技术、知识经济和跨国公司的飞速发展给国际资本流动带来了深刻的影响。国际资本流动的新旧因素在全球化条件下重新组合,并在跨国公司的全球经营战略的推力和以信息技术、知识经济、企业生态环境为代表的创新因素的引力作用下,形成以跨国公司为主体的,沿着价值链的各个节点组建全球生产的驱动型动力机制,最后以中国为例探讨影响中国引资的因素及对策和建议。     

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.     

当代中国的社会变迁与女性犯罪

当前我国处于急剧的社会变迁时期,社会变迁带来的社会角色的变迁、社会地位的分化、社会整合力的减弱以及社会流动的加速,是当代女性犯罪不断上升和蔓延的重要原因.本文从社会变迁角度探讨当代女性犯罪的复杂成因.     

劳动力密集地区非均衡流动——对河南省固始县劳动力流动及县域经济发展的调查

在劳动力密集的河南省,大量输出劳动力可以迅速促进当地经济的发展和人民生活水平的提高,但人们往往忽视从长远看过量输出导致的非均衡流动却可能束缚输出地经济的发展,笔者通过对河南省信阳市固始县为期13天的调研,对这一问题进行了研究。本文描述了劳动力的大量输出对当地经济发展的促进,着重分析了当地劳动力流动的原因,并分析了非均衡流动给当地经济未来发展带来的三个问题。文章将在前半部分从劳动力流动的环境动力和劳动力流动去向的角度,结合劳动力流动的相关理论,对谷围村劳动力流动情况进行分析,并用"吸出模型"解释劳动力非均衡流动带来的问题和产生的原因。文章后半部分从劳动力大量输出对当地县域经济影响的角度分析了在劳动力密集的固始县企业招工难且劳动力成本高于外地的奇怪现象。最后,文章对固始县为实现劳动力均衡流动而采取的措施进行了研究,总结出劳动力过量输出地区的经济发展模式即"固始模式",并对该模式进行了分析。     

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.     

B股与H股及红筹股之间的溢出效应与信息流动

运用向量GARCH-M模型检验了B股与H股及红筹股之间的溢出效应与信息流动.实证结果表明:红筹股对沪深市B股、H股对沪市B股的收益和波动溢出效应均显著存在,而反向的溢出效应均不显著,表明信息是从红筹股向沪深市B股、从H股向沪市B股单向流动的;深市B股与H股之间相互的收益溢出效应均存在,而波动溢出效应均不存在,信息在H股和深市B股之间的流动情况不明显;在信息流动过程中,红筹股始终处于信息领先地位.