一类易逝品非线性成本库存模型

本文采用指数函数模型来描述易逝品的成本随时间变化的特征,首先求出模型在基本假设条件下的最优订货量和订货成本,然后推导出了在订货商允许缺货、市场需求可变和订货周期可变三个扩展情景下的成本函数;最后通过计算机仿真实验,从经济性和时间效率两方面评价了本文的指数模型相对于经典EOQ模型的优势。     

多重线性回归模型的贝叶斯预报分析

多重线性回归模型的贝叶斯预报分析是贝叶斯线性模型理论的重要组成部分。通过模型系统的统计结构，证明了矩阵正态-Wishart分布为模型参数的共轭先验分布；利用贝叶斯定理，根据模型的样本似然函数和参数的先验分布推导了参数的后验分布；然后，从数学上严格推断了模型的预报分布密度函数，证明了模型预报分布为矩阵t分布。研究结果表明：由于参数先验分布的作用，样本的预报分布与其原统计分布有着本质性的差异，前服从矩阵正态分布，而后为矩阵t分布。     

古诺竞争的选址模型研究

在Paul提出的圆环形城市模型基础上,通过引入成本分布函数,扩展了Pal和Matsushima的模型,建立了一个新的带有成本因子的选址与产量竞争的双寡头竞争模型.结果表明:如果成本分布函数是常数,那么两企业均衡地分布于圆环形城市将达到完美的纳什均衡;如果成本分布函数是严格凸函数,当运输系数较小时,企业将在产品成本分布函数最小点处集聚,并各自达到利润最大化.     

Zadeh的隶属函数对似然方法、语义通信和统计学习的意义

流行的似然方法不合适数据先验分布(即信源)可变场合。为此,我们把Zadeh的隶属函数看做预测模型,用隶属函数和可变信源产生似然函数,用平均对数标准(normalized)似然度定义语义信息测度。这样可以保证:(1)坚持使用最大似然准则;(2)预测模型适合信源可变场合;(3)得到的语义贝叶斯预测兼容贝叶斯定理;(4)预测模型能表达语义,便于理解。一组隶属函数构成一个语义信道,优化隶属函数就是使语义信道匹配Shannon信道,产生多标签模糊分类。文中介绍了通过两种信道相互匹配求解最大似然度的迭代算法。几个例子显示这种算法用于检验、估计和混合模型时,收敛快速且可靠。     

上市公司审计费用率能作为审计定价吗?——审计费用与审计费用率的比较研究

本文以披露了2000年、2001年年度财务报告审计费用的上市公司为样本,使用回归分析方法,研究了审计费用、审计费用率的影响因素并进行了比较。研究表明,审计费用与审计费用率具有相同的影响因素,各影响因素对审计费用率的解释度远远高于对审计费用的解释度,资产规模对审计费用的解释度达到74%。由此说明,从审计费用率角度研究审计定价不仅等价于从审计费用角度研究审计定价,而且效果更明显。     

排队论在销售管理中的应用

销售性企业如何才能降低销售时的综合成本是一个值得研究的问题.以排队论为基础对这一问题展开讨论,分析了顾客到达企业时的排队方式,得出了单队多服务通道要比多队多服务通道排队方式要优;分析了系统的服务规则及评价指标,并建立了一个输入率可变、服务率可变且先到先服务的、有不耐烦顾客的销售模型,以及一个输入率可变、服务率可变且有非强占优先权的销售模型,分别得出了系统的平均服务率及顾客在系统中的平均等待时间,从而建立了企业销售时的综合成本函数,并结合实例给出了求综合成本函数最小值的方法.     

基于可变模糊集理论的城市用地适用性评估

依据可变模糊集理论,构造了城市用地适用性评估的相对差异函数模型和相对隶属函数模型,建立了求解综合相对隶属度的可变模糊评估模型,并应用该模型对2组待开发利用用地适用性进行了评估.研究表明:可变模糊评估是依据对立相对隶属函数来描述模糊概念,通过参数的可变性,自我验证可变模糊评估方法的可靠性,并通过级别特征值表达了评估对象属于某级别的程度,使评估结果更为精细,从而为城市用地适用性评估提供了一种有效的新方法.     

基于模糊规划算法求多层线性规划的折中最优解

本文利用模糊规划算法求解多层线性规划问题。在对问题的第l层进行求解时,确定前l层目标函数和前l-1层决策变量的隶属函数,通过隶属函数表明各层决策者对于目标函数和决策变量的满意水平。建立使得最差满意水平最大化的线性规划模型求得折中最优解。通过数值算例验证算法的可行性。     

绿色消费视角的政企合作绿色技术创新动态决策

通过构建微分博弈模型,对比分析无成本分担、成本分担及集中式决策情形下,政府和企业最优反馈均衡策略和收益最优值函数.研究发现,成本分担情形下,企业绿色技术创新努力程度要高于无成本分担情形,该努力程度不仅取决于自身的边际收益,还与政府边际收益、企业技术创新社会收益正相关;集中决策情形下,政府、企业的最优努力水平和系统收益最优值均达到最高水平,且与二者的边际收益之和正相关;社会公众绿色消费的关注程度对系统收益具有正向影响,且影响程度边际递增;各参与主体努力程度对企业社会声誉影响越大,则合作系统收益越大.     

Weibull分布步进应力加速寿命试验的Bayes估计

本文研究了定时和定数截尾情形CE模型下Weibull分布场合步进应力加速寿命试验的Bayes估计.利用加速系数和加速方程将各种加速应力水平下的尺度参数换算为正常应力水平下的尺度参数,从而获得含正常应力下尺度参数的似然函数.在参数先验的选取时,尺度参数和加速系数分别取共轭先验和无信息先验,当形状参数m<1和m>1时分别取Beta分布和Gamma分布作为其先验.在平方损失下,利用Gibbs抽样和切片抽样给出了该模型参数的Bayes估计.最后,通过Monte Carlo模拟表明该Bayes估计是有效的.     

Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods

In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with “vanilla” versions of simulated annealing, genetic algorithm and tabu search.     

A deteriorating cold standby repairable system with priority in use

In this paper, a cold standby repairable system consisting of two dissimilar components and one repairman is studied. In this system, it is assumed that the working time distributions and the repair time distributions of the two components are both exponential and component 1 is given priority in use. After repair, component 2 is “as good as new” while component 1 follows a geometric process repair. Under these assumptions, using the geometric process and a supplementary variable technique, some important reliability indices such as the system availability, reliability, mean time to first failure (MTTFF), rate of occurrence of failure (ROCOF) and the idle probability of the repairman are derived. A numerical example for the system reliability R(t) is given. And it is considered that a repair-replacement policy based on the working age T of component 1 under which the system is replaced when the working age of component 1 reaches T. Our problem is to determine an optimal policy T^∗ such that the long-run average cost per unit time of the system is minimized. The explicit expression for the long-run average cost per unit time of the system is evaluated, and the corresponding optimal replacement policy T^∗ can be found analytically or numerically. Another numerical example for replacement model is also given.     

Reference Bayesian analysis of inverse Gaussian degradation process

In this paper, objective Bayesian method is applied to analyze degradation model based on the inverse Gaussian process. Noninformative priors (Jefferys prior and two reference priors) for model parameters are obtained and their properties are discussed. Moreover, we propose a class of modified reference priors to remedy weaknesses of the usual reference priors and show that the modified reference priors not only have proper posterior distributions but also have probability matching properties for model parameters. Gibbs sampling algorithms for Bayesian inference based on the Jefferys prior and the modified reference priors are studied. Simulations are conducted to compare the objective Bayesian estimates with the maximum likelihood estimates and subjective Bayesian estimates and shows better performance of the objective method than the other two estimates especially for the case of small sample size. Finally, two real data examples are analyzed for illustration.     

Multistate components assignment problem with optimal network reliability subject to assignment budget

The typical assignment problem for finding the optimal assignment of a set of components to a set of locations in a system has been widely studied in practical applications. However, this problem mainly focuses on maximizing the total profit or minimizing the total cost without considering component’s failure. In practice, each component should be multistate due to failure, partially failure, or maintenance. That is, each component has several capacities with a probability distribution and may fail. When a set of multistate components is assigned to a system, the system can be treated as a stochastic-flow network. The network reliability is the probability that d units of homogenous commodity can be transmitted through the network successfully. The multistate components assignment problem to maximize the network reliability is never discussed. Therefore, this paper focuses on solving this problem under an assignment budget constraint, in which each component has an assignment cost. The network reliability under a components assignment can be evaluated in terms of minimal paths and state-space decomposition. Subsequently an optimization method based on genetic algorithm is proposed. The experimental results show that the proposed algorithm can be executed in a reasonable time.     

有优先维修权和优先使用权的冷储备系统的几何过程模型

本文研究了一个由两个部件和一个维修工组成的可修型冷储备系统.假设两个部件的工作时间和维修时间都服从指数分布,对部件2的维修是修旧如新而对部件1则是几何维修,且对部件1给予优先使用和优先维修的权利,在这些假定下,我们运用几何过程理论和补充变量方法,得到了一些重要的可靠性指标如系统可靠度、可用度、系统首次故障前平均工作时间和系统瞬时故障率等.最后还给出了维修工空闲的概率.     

Adaptive Markov Chain Monte Carlo for Bayesian Variable Selection

We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions arising from Bayesian variable selection problems. Point-mass mixture priors are commonly used in Bayesian variable selection problems in regression. However, for generalized linear and nonlinear models where the conditional densities cannot be obtained directly, the resulting mixture posterior may be difficult to sample using standard MCMC methods due to multimodality. We introduce an adaptive MCMC scheme that automatically tunes the parameters of a family of mixture proposal distributions during simulation. The resulting chain adapts to sample efficiently from multimodal target distributions. For variable selection problems point-mass components are included in the mixture, and the associated weights adapt to approximate marginal posterior variable inclusion probabilities, while the remaining components approximate the posterior over nonzero values. The resulting sampler transitions efficiently between models, performing parameter estimation and variable selection simultaneously. Ergodicity and convergence are guaranteed by limiting the adaptation based on recent theoretical results. The algorithm is demonstrated on a logistic regression model, a sparse kernel regression, and a random field model from statistical biophysics; in each case the adaptive algorithm dramatically outperforms traditional MH algorithms. Supplementary materials for this article are available online.     

On a Class of Shrinkage Priors for Covariance Matrix Estimation

We propose a flexible class of models based on scale mixture of uniform distributions to construct shrinkage priors for covariance matrix estimation. This new class of priors enjoys a number of advantages over the traditional scale mixture of normal priors, including its simplicity and flexibility in characterizing the prior density. We also exhibit a simple, easy to implement Gibbs sampler for posterior simulation, which leads to efficient estimation in high-dimensional problems. We first discuss the theory and computational details of this new approach and then extend the basic model to a new class of multivariate conditional autoregressive models for analyzing multivariate areal data. The proposed spatial model flexibly characterizes both the spatial and the outcome correlation structures at an appealing computational cost. Examples consisting of both synthetic and real-world data show the utility of this new framework in terms of robust estimation as well as improved predictive performance. Supplementary materials are available online.     

Noninformative Priors for Multivariate Linear Calibration

This paper derives a class of first order probability matching priors and a complete catalog of the reference priors for the general multivariate linear calibration problem. In an important special case, a complete characterization of first order probability matching priors is given, and a fairly general class of second order probability matching priors is also provided. Orthogonal transformations (1987, D. R. Cox and N. Reid, J. Roy. Statist. Soc. Ser. B49, 1–18) are found to facilitate the derivations. It turns out that under orthogonal parameterization, reference priors (including Jeffreys' prior) are first order probability matching priors for unidimensional multivariate linear calibration. Also, in univariate linear calibration, the prior of W. G. Hunter and W. F. Lamboy (1981, Technometrics23, 323–350) is a second order probability matching prior.     

An optimal repeat inspection plan with several classifications

Multicharacteristic critical components exist in many systems. Such components could be a part of an aircraft, space shuttle or a gas ignition system. A component is critical if it causes disaster or a very high cost upon failure. In this paper, a new inspection plan for critical multicharacteristic components is presented. A mathematical model that depicts the plan is developed. An algorithm is proposed for finding the optimal number of repeat inspections and the sequence of characteristics for inspection that minimizes expected total cost per accepted component. The expected cost consists of the cost of inspection and the cost of misclassifications. The inspection plan and the model developed generalize existing models in the literature and provide a more realistic formulation. An example is given to demonstrate the plan and the model.     

Bayesian Variable Selection on Model Spaces Constrained by Heredity Conditions

This article investigates Bayesian variable selection when there is a hierarchical dependence structure on the inclusion of predictors in the model. In particular, we study the type of dependence found in polynomial response surfaces of orders two and higher, whose model spaces are required to satisfy weak or strong heredity conditions. These conditions restrict the inclusion of higher-order terms depending upon the inclusion of lower-order parent terms. We develop classes of priors on the model space, investigate their theoretical and finite sample properties, and provide a Metropolis–Hastings algorithm for searching the space of models. The tools proposed allow fast and thorough exploration of model spaces that account for hierarchical polynomial structure in the predictors and provide control of the inclusion of false positives in high posterior probability models.