关于在高校试行会计委派制若干问题的思考

高校因经济管理方式和运行模式的转变 ,使得深化财务改革、规范财经秩序、确保国有资产的安全完整、扼制会计信息失真现象等成为当前的主要任务。会计委派制的实施有助于加强会计工作的监督和管理 ,起到从源头上控制的作用 ,因而在高校开展这项工作势在必行     

基于贝叶斯神经网络的非参数回归

提高神经网络模型推广能力的关键是控制模型的复杂度。该文探索了贝叶斯神经网络的非参数回归的建模方法,通过融入模型参数的先验知识,在给定数据样本及模型假设下进行后验概率的贝叶斯推理,使用马尔可夫链蒙特卡罗算法来优化模型控制参数,实现了对神经网络模型中不同部分复杂度的控制,获得了模型参数的后验分布及预测分布。在5个含噪二维函数回归问题上的应用显示了模型的复杂度能根据数据的复杂度而自适应调整,并给出了较好的预测结果。     

基于马尔可夫链的产品市场保有量预测模型

售后服务机构为了准确地准备库存,都需要预测产品的市场保有量。基于马尔可夫链建立的状态概率矩阵模型,与抽样调查、时间序列法、回归分析法等方法相比,更能准确预测市场产品保有量。与传统产品相比较,更新换代速度快的电子产品,要预测其市场保有量,更需要利用状态概率矩阵模型。     

Estimating rating transition probabilites with missing data

In this article we provide a rigorous treatment of one of the central statistical issues of credit risk management. GivenK-1 rating categories, the rating of a corporate bond over a certain horizon may either stay the same or change to one of the remainingK-2 categories; in addition, it is usually the case that the rating of some bonds is withdrawn during the time interval considered in the analysis. When estimating transition probabilities, we have thus to consider aK-th category, called withdrawal, which contains (partially) missing data. We show how maximum likelihood estimation can be performed in this setup; whereas in discrete time our solution gives rigorous support to a solution often used in applications, in continuous time the maximum likelihood estimator of the transition matrix computed by means of the EM algorithm represents a significant improvement over existing methods.     

On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.     

公共财政与国家财政

公共财政与国家财政是人们对财政发展不同阶段的具体认识,公共财政包含在国家财政的内涵之中,是市场经济条件下的国家财政,所以应实行公共财政改革。     

关于民间非正规金融的若干分析

20世纪80年代以来,随着改革开放的顺利进行,我国个体私营经济发展迅速,信贷需求日益旺盛,而正规金融机构往往不能满足其资金需求,只能转而求助于非正规金融机构。在此经济体制改革的大背景下,本文在对非正规金融进行较为深入和全面分析的基础上,着重分析了民间非正规金融机构的成因,并提出了相关的对策。     

蒂布特模型启迪下的可流动公共产品供给

中国目前正处于城乡公共产品统筹供给的关键时期,研究符合中国国情的公共产品供给理论具有重要意义。蒂布特模型是关于公共产品供给机制的理论研究,其政策含义是政府要采取促进居民流动的方式来提高公共产品的配置效率。由于该模型所作假设的现实性较弱,其结论和应用有一定局限性。根据流动程度,可以把公共产品划分为可流动公共产品和不可流动公共产品两类,可以通过可流动公共产品的流动来提高公共产品的配置效率。中国目前在城乡可流动公共产品优化配置方面有较大发展空间。     

一种基于神经网络和决策树的信用评估新方法

分析了数据挖掘在国内外金融领域的应用及研究现状,提出了一种基于神经网络和决策树相结合的信用评估新方法。该方法通过RBF神经网络,进行条件属性裁减,并利用决策树抽取出评估规则。此方法利用神经网络的“黑箱”工作特性,选择重要条件属性,并利用决策树自动生成评估规则,大大提高了信用评估的效率和客观性。     

乡镇企业家的供求问题研究

市场制度的建立程度、企业创立条件的高低以及企业家人力资本产权的受保护程度影响乡镇企业家的需求与供给 ,导致乡镇企业创新乏力和资产流失。健全乡镇企业家成长的市场环境和制度环境 ,建立有效的激励机制 ,是保证乡镇企业家的有效需求和供给的真正解决办法。