Dynamic reliability models with conditional proportional hazards

A dynamic approach to the stochastic modelling of reliability systems is further explored. This modelling approach is particularly appropriate for load-sharing, software reliability, and multivariate failure-time models, where component failure characteristics are affected by their degree of use, amount of load, or extent of stresses experienced. This approach incorporates the intuitive notion that when a set of components in a coherent system fail at a certain time, there is a jump from one structure function to another which governs the residual lifetimes of the remaining functioning components, and since the component lifetimes are intrinsically affected by the structure function which they constitute, then at such a failure time there should also be a jump in the stochastic structure of the lifetimes of the remaining components. For such dynamically-modelled systems, the stochastic characteristics of their jump times are studied. These properties of the jump times allow us to obtain the properties of the lifetime of the system. In particular, for a Markov dynamic model, specific expressions for the exact distribution functions of the jump times are obtained for a general coherent system, a parallel system, and a series-parallel system. We derive a new family of distribution functions which describes the distributions of the jump times for a dynamically-modelled system.     

Sample selection in radiocarbon dating

Archaeologists working on the island of O'ahu, Hawai'i, use radiocarbon dating of samples of organic matter found trapped in fish-pond sediments to help them to learn about the chronology of the construction and use of the aquicultural systems created by the Polynesians. At one particular site, Loko Kuwili, 25 organic samples were obtained and funds were available to date an initial nine. However, on calibration to the calendar scale, the radiocarbon determinations provided date estimates that had very large variances. As a result, major issues of chronology remained unresolved and the archaeologists were faced with the prospect of another expensive programme of radiocarbon dating. This paper presents results of research that tackles the problems associated with selecting samples from those which are still available. Building on considerable recent research that utilizes Markov chain Monte Carlo methods to aid archaeologists in their radiocarbon calibration and interpretation, we adopt the standard Bayesian framework of risk functions, which allows us to assess the optimal samples to be sent for dating. Although rather computer intensive, our algorithms are simple to implement within the Bayesian radiocarbon framework that is already in place and produce results that are capable of direct interpretation by the archaeologists. By dating just three more samples from Loko Kuwili the expected variance on the date of greatest interest could be substantially reduced.     

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.     

一类供应链的线性转移支付激励机制研究

针对不同的供应链设计不同的契约是供应链协作的一个重要手段。在由一个零售商和一个供应商组成的供应链中,零售商的促销努力水平对供应链的效率有重大的影响,本文提出了一种线性转移支付激励机制,且证明了在该机制的作用下,零售商将付出对整个供应链最优的促销努力水平,这时供应链的总收益将得到提高,而对新增收益的划分则取决于供应商和零售商之间的讨价还价能力。     

The impact of supply chain management practices on competitive advantage and organizational performance

Effective supply chain management (SCM) has become a potentially valuable way of securing competitive advantage and improving organizational performance since competition is no longer between organizations, but among supply chains. This research conceptualizes and develops five dimensions of SCM practice (strategic supplier partnership, customer relationship, level of information sharing, quality of information sharing, and postponement) and tests the relationships between SCM practices, competitive advantage, and organizational performance. Data for the study were collected from 196 organizations and the relationships proposed in the framework were tested using structural equation modeling. The results indicate that higher levels of SCM practice can lead to enhanced competitive advantage and improved organizational performance. Also, competitive advantage can have a direct, positive impact on organizational performance.     

考虑质量失误的供应链博弈模型研究

在理性的制造商和供应商组成的二级供应链中,考虑存在产品质量失误的情况下,通过最优化方法选择中间品的质量水平和制造商的检测水平,以实现供应链的利润最大化.并结合实际情况,根据质量内部、外部失误的不同承担方式,讨论了三种质量损失承担方式,得到在整体损失由两者共同承担的情况下,通过制定合理的内、外部损失分配系数,可以使两者选择水平的纳什均衡解与供应链的全局最优解相符,从而实现供应链全局最优和局部最优的一致.所得结论对指导供应链的有效运作有着较好的实际意义和参考价值.     

信息共享程度对物流外包激励契约的影响

本文分析了由供应商、制造商和第三方物流企业(3PLs)组成的物流外包系统,考虑了供应商和制造商信息共享程度的影响,建立了协同工作环境下的激励契约模型.结果表明,委托人偏好低努力水平时,信息共享对激励契约没有影响;委托人偏好高努力水平时,信息共享能更好的激励代理人选择委托人希望的行动.信息不共享时,3PLs只有提高对供应商努力水平的积极性;信息共享时,3PLs收入波动性增大,提高对供应商和对制造商的努力水平都能使其获得帕累托改进.本研究为物流成本在供应链成员中的分摊提供了理论依据,并提出了模型改进和研究建议.     

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MCMC稳态模拟的贝叶斯经验费率厘定信用模型

B黨lmann-Straub model is one of the most famous applications of the Bayesian method for the experience rate making.However,by the traditional B黨lmann-Straub model one cannot get the unbiased posterior estimation of the parameters when there is not sufficient prior information for the structural parameters;What's more,the difficult of computing high dimension numeration limits the application of Bayesian method.This paper introduces the Markov chain Monte Carlo simulaton method based on the Gibbs sampling after analyzing the structure of the B黨lmann-Straub model and sets up the Bayesian credibility model for estimating the predictive risk premium.Also by using the results of the numeration analysis,this paper proves that from this model one can get the posterior distributions of the parameters dynamically and the posterior estimation of the censoring parameters in the situation that exists unknown parameters,as well as improve the precision of the numeration,which can be helpful to find the heterogeneity of the premium.