On Some Optimal Bayesian Nonparametric Rules for Estimating Distribution Functions

In this paper, we present a novel approach to estimating distribution functions, which combines ideas from Bayesian nonparametric inference, decision theory and robustness. Given a sample from a Dirichlet process on the space (𝒳, A), with parameter η in a class of measures, the sampling distribution function is estimated according to some optimality criteria (mainly minimax and regret), when a quadratic loss function is assumed. Estimates are then compared in two examples: one with simulated data and one with gas escapes data in a city network.     

Asymptotics for Statistical Treatment Rules

This paper develops asymptotic optimality theory for statistical treatment rules in smooth parametric and semiparametric models. Manski (2000, 2002, 2004) and Dehejia (2005) have argued that the problem of choosing treatments to maximize social welfare is distinct from the point estimation and hypothesis testing problems usually considered in the treatment effects literature, and advocate formal analysis of decision procedures that map empirical data into treatment choices. We develop large‐sample approximations to statistical treatment assignment problems using the limits of experiments framework. We then consider some different loss functions and derive treatment assignment rules that are asymptotically optimal under average and minmax risk criteria.     

Robust Bayesian prediction under a general linear-exponential posterior risk function and its application in finite population

We consider robust Bayesian prediction of a function of unobserved data based on observed data under an asymmetric loss function. Under a general linear-exponential posterior risk function, the posterior regret gamma-minimax (PRGM), conditional gamma-minimax (CGM), and most stable (MS) predictors are obtained when the prior distribution belongs to a general class of prior distributions. We use this general form to find the PRGM, CGM, and MS predictors of a general linear combination of the finite population values under LINEX loss function on the basis of two classes of priors in a normal model. Also, under the general ε-contamination class of prior distributions, the PRGM predictor of a general linear combination of the finite population values is obtained. Finally, we provide a real-life example to predict a finite population mean and compare the estimated risk and risk bias of the obtained predictors under the LINEX loss function by a simulation study.     

基于后悔理论的混合型多属性案例决策方法

本文针对决策者的后悔规避行为对案例决策的影响,提出了一种基于后悔理论的混合型多属性案例决策方法。首先,针对案例属性的相似度测算,将案例属性的范围拓展到定性数据、清晰数、区间数、语言变量和区间直觉模糊数五种类型;然后,在案例原有效用基础上考虑决策者的后悔-欣喜值来测算各案例的感知效用;进一步,通过案例相似度对感知效用加权求和得到备选方案的综合感知效用,并依据综合感知效用大小对备选方案进行排序;最后通过一个实例,验证了上述方法的可行性和有效性。     

具有遗憾值约束的鲁棒供应链网络设计模型研究

考虑不确定性环境,研究战略层次的供应链网络鲁棒设计问题,目标是设计参数发生摄动时,供应链性能能够保持稳健性。基于鲁棒解的定义,建立从上游供应商选择到下游设施选址-需求分配的供应链网络设计鲁棒优化模型;提出确定遗憾值限定系数上限和下限的方法,允许决策者调节鲁棒水平,选择多种供应链网络结构;通过模型分解与协调,设计了供应链节点配置的禁忌搜索算法。算例的计算结果表明了禁忌搜索算法具有良好的收敛特性,以及在处理大规模问题上的优越性;同时也反映了利用鲁棒优化模型进行供应链网络设计,可以有效规避投资风险。     

预期后悔、归因风格对购买决策中信息加工的影响

在以往研究的基础上,引入预期后悔情绪（高、低）和归因风格（积极归因风格、消极归因风格）变量,以购买汽车为决策任务,采用决策研究的信息板技术呈现实验任务,探讨了预期后悔情绪与归因风格对购买决策信息加工的影响。结果显示：（1）在信息加工时间和信息搜索深度上,预期后悔情绪与归因风格主效应以及交互作用显著,积极归因风格比消极归因风格的被试更容易受到预期后悔情绪的影响,表现为更长时间的信息加工和更深的信息搜索;（2）在信息搜索模式上,预期后悔情绪与归因风格的交互作用不显著,高预期后悔情绪的被试多倾向于基于属性的信息搜索模式。     

论《聊斋志异》中的商人形象

蒲松龄结合自己的人生际遇,参照明清时期商人的现状和通俗文学作品中的商贾形象,塑造了三类各具特色的商人形象:诚实本分的小贩,道德败坏的奸商,重义轻财的儒商。从这些商贾形象可以看出蒲松龄对商业和商人的认识是正面的,同时,不难发现《聊斋志异》里的商人也具有缺乏自信心、自主性、个性不够鲜明的特点。     

Regret theory with general choice sets

The regret theory of choice under uncertainty proposed by Loomes and Sugden has performed well in explaining and predicting violations of Expected Utility theory. The original version of the model was confined to pairwise choices, which limited its usefulness as an economic theory of choice. Axioms for a more general form of regret theory have been proposed by Loomes and Sugden. In this article, it is shown that a simple nonmanipulability requirement is sufficient to characterize the functional form for regret theory with general choice sets. The stochastic dominance and comparative static properties of the model are outlined. A number of special cases are derived in which regret theory is equivalent to other well-known theories of choice under uncertainty.I would like to thank Graham Loomes, Robert Sugden, Peter Wakker, and Nancy Wallace for helpful comments and criticism.     

Empirical Bayes testing for guarantee lifetime: Non identical components case

This article considers an empirical Bayes testing problem for the guarantee lifetime in the two-parameter exponential distributions with non identical components. We study a method of constructing empirical Bayes tests under a class of unknown prior distributions for the sequence of the component testing problems. The asymptotic optimality of the sequence of empirical Bayes tests is studied. Under certain regularity conditions on the prior distributions, it is shown that the sequence of the constructed empirical Bayes tests is asymptotically optimal, and the associated sequence of regrets converges to zero at a rate O(n^{? 1 + 1/[2(r + α) + 1]}) for some integer r ? 0 and 0 ? α ? 1 depending on the unknown prior distributions, where n is the number of past data available when the (n + 1)st component testing problem is considered.     

零售商长期后悔规避的供应链网络动态均衡

本文研究了基于时变随机需求的供应链网络动态均衡。由于需求的不确定性和季节性，网络中的零售商会考虑缺货后悔和存货后悔的负效用，基于长期后悔规避效用做出最优决策。利用变分不等式，刻画了网络中制造商层、考虑长期后悔规避效用的零售商层和需求市场层的均衡决策行为。通过数值算例，基于三种长期后悔情形分析了零售商长期后悔规避行为对其均衡订购量、利润、后悔值的影响。研究结果表明，零售商的长期后悔规避程度在不断增加、保持不变，不断减少这三种情况下，对其均衡订购量、利润、后悔值的影响不同。研究结论将指导供应链网络中企业，基于长期后悔规避制定最优决策。