Application of Markov chain Monte Carlo methods to modelling birth prevalence of Down syndrome

Data collected before the routine application of prenatal screening are of unique value in estimating the natural live-birth prevalence of Down syndrome. However, much of these data are from births from over 20 years ago and they are of uncertain quality. In particular, they are subject to varying degrees of underascertainment. Published approaches have used ad hoc corrections to deal with this problem or have been restricted to data sets in which ascertainment is assumed to be complete. In this paper we adopt a Bayesian approach to modelling ascertainment and live-birth prevalence. We consider three prior specifications concerning ascertainment and compare predicted maternal-age-specific prevalence under these three different prior specifications. The computations are carried out by using Markov chain Monte Carlo methods in which model parameters and missing data are sampled.     

Performance measurement with classification information: an enhanced additive DEA model

This paper presents an improved measurement tool for evaluating performance of branches within a major Canadian bank. While there have been numerous previous studies of performance in the banking industry, particularly at the branch level, this study is different in a very significant way: specifically two kinds of data are used to develop the model. The first type of data is that related to standard transactions, available from any bank; such have formed the basis of numerous previous studies. The second type of data, obtained from the site studied, is classification information, based on branch consultant/expert judgment as to good and poor performance of branches. The purpose herein is to present a modified version of an existing benchmarking model, data envelopment analysis (DEA), and to show how this tool is applied in the banking industry. The mechanism used herein to incorporate expert knowledge within the DEA framework is to first apply a discriminant or classification tool, to quantify the functional relation that best captures the expert's mental model for performance. The outcome of this first phase is an orientation of variables to aid in the definition of inputs and outputs. The resulting orientation then defines the DEA model that makes up the second phase of the model.     

曲线箱梁桥实用设计方法的研究

曲线箱梁桥目前还缺乏比较实用的设计方法．本文对曲线箱梁桥的现有设计方法作了研究，并推荐一种实用设计方法：采用空间梁单元，每个节点取六个自由度；等效梁格的模拟考虑了横向梁格附近有无横隔梁的情况．结果证明，这是一种简便、较精确的设计方法．     

On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first-order reliability method (FORM)^(1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al. ⁽⁴⁾ on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al. ⁽⁴⁾ The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al. ⁴ than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.     

金融工程中资产收益的连续时间模型评述

总结了在过去30年中金融资产收益连续时间模型的发展及主要成果,讨论了迄今连续时间模型参数估计的主要方法,其中特别讨论了MCMC方法;最后指出了现在和未来该领域研究所面临的主要课题。     

基于向量夹角余弦的组合预测模型的性质研究

基于向量夹角余弦的组合预测是一种相关性的组合预测模型,它是研究组合预测方法的一个新途经.针对基于向量夹角余弦准则下组合预测模型,研究它的基本结构特征.首先提出新的优性组合预测、预测方法优超、冗余度等概念.然后探讨了非劣性组合预测、优性组合预测以及冗余预测方法的存在性,并给出冗余信息的判定定理.最后进行实例分析,表明该方法有较大的实际应用价值.     

供应链绩效评价指标体系与评价方法研究

供应链管理(SCM)与现行企业模式有较大区别,其绩效评价指标体系的建立与评价方法也就有其特殊性,目前这方面的研究还略显不足。本文在综合分析现有的SCM评价指标体系的基础上,初步建立了一套适合我国SCM绩效评价的指标体系。同时,给出了SCM绩效评价的多级动态模糊综合评价方法。     

人力资源个体价值计量方法——当期实现价值系数法

由于人力资源自身具有复杂性、不确定性的特点,使得人力资源价值难以计量。而准确的计量人力资源价值,尤其是人力资源的个体价值是人力资源会计能否实施的关键因素。针对这个问题,本文在分析前人成果的基础上提出了人力资源当期实现价值系数法计量模型。并通过实例验证了计量模型的实用性。     

基于医疗质量的公立医院绩效体系构建研究

目的：运用德尔菲法构建一套科学、可操作的公立医院绩效评价体系。方法：采用文献法研究国内外绩效评价理论,结合问卷结果形成初始量表。通过德尔菲法对指标进行两轮筛选及优化。结果：初步形成由“社会效益”、“质量安全”、“运行效益与效率”为基本结构的指标体系,包括3个一级指标、13个二级指标和72个三级指标。结论：该指标体系重点关注医院公益性及内涵质量,对公立医院绩效评价具有一定参考意义。     

基于灰关联约束超效率DEA的重点实验室评估研究——以河北省省级重点实验室为例

目前国内外对重点实验室的评估主要采用专家评价等软评价方法,较少考虑科技投入和科技产出之间的关联关系。在对国内外重点实验室评估方法进行深入研究的基础上,利用灰色关联度分析指标内在的关联性,将其作为限制条件加入超效率DEA评价模型中,构建了基于灰色关联权重约束的超效率DEA评价模型,并应用于河北省省级重点实验室评估的实证分析。研究结果显示,此模型与传统的CCR模型和超效率DEA模型相比,既可以揭示科技投入和科技产出之间的内在联系,又可以保持DEA评价的客观性,与此同时,还可实现不同实验室评估结果的全排序,使得评估结果更加符合实际。