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。     

论第三人侵害缔约利益制度的设立

缔约利益是指缔约磋商当事人在合同缔结过程中存在着的一种特殊利益，其具有实存性、绝对性的特点。当缔约利益受到第三人侵害时，利益主体也会产生“利益复位”的诉求。大陆法系既有的理论与制度并不能有效解决第三人侵害缔约利益的问题。《美国侵权法》重述第37章的规定值得我们借鉴。未来的《侵权责任法》应当建立第三人侵害缔约利益制度。     

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。     

经济全球化的挑战与中国的对策

经济全球化作为一个客观的历史进程,对国家的经济主权形成了巨大的挑战与影响,作为发展中国家的我国,必须适应这种历史潮流,科学认识与理性应对,以便更有效地维护国家利益。     

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.     

试析康德“美的分析”四命题的独创性

康德认为“审美”既不涉及概念,也不涉及任何法规,而是主观的行为。“审美”不涉利害,“美感”和“一般快感”以及“完善”不同之处,在于“美感”绝不涉及主体的欲望或需要。在此基础上,康德进而提出“审美”不经概念而具普遍性,“美”是在人类具有“共同感受力”的基础上“普遍可传达”的一种感受。同时,“审美”应该无目的而符合目的性,并具有不依赖概念的必然性。康德还通过对“纯粹美”和“依存美”的定义和比较,进一步开展对“理想美”的探求。     

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.     

提高大学生课堂昕课兴趣的探讨与实践

课堂教学由教师和学生组成,教师与学生默契配合是调动学生学习积极性的重要因素。丰富的教学内容,生动活泼的讲课节律,活跃的课堂气氛,科学的考试方法等,均影响着学生的听课兴趣。     

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.