Measuring and Achieving Equity in Multiperiod Emergency Material Allocation

Emergency material allocation is an important part of postdisaster emergency logistics that is significant for improving rescue effectiveness and reducing disaster losses. However, the traditional single‐period allocation model often causes local surpluses or shortages and high cost, and prevents the system from achieving an equitable or optimal multiperiod allocation. To achieve equitable allocation of emergency materials in the case of serious shortages relative to the demand by victims, this article introduces a multiperiod model for allocation of emergency materials to multiple affected locations (using an exponential utility function to reflect the disutility loss due to material shortfalls), and illustrates the relationship between equity of allocations and the cost of emergency response. Finally, numerical examples are presented to demonstrate both the feasibility and the usefulness of the proposed model for achieving multiperiod equitable allocation of emergency material among multiple disaster locations. The results indicate that the introduction of a nonlinear utility function to reflect the disutility of large shortfalls can make the material allocation fairer, and minimize large losses due to shortfalls. We found that achieving equity has a significant but not unreasonable impact on emergency costs. We also illustrate that using differing utility functions for different types of materials adds an important dimension of flexibility.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Estimation of the marginal expected shortfall under asymptotic independence

We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby.     

Subjective utility with upper and lower probabilities on finite states

This article is concerned with thresholds of discrimination of preference judgments under uncertainty. We establish an axiomatic characterization for a threshold representation, where thresholds are represented by inexact measurement of subjective probabilities, i.e., upper and lower probabilities. Since upper and lower probabilities need not be additive, the representational form adopts the Choquet integration.     

Book Reviews

Books reviewed:
M Hollander and D Wolfe, Nonparametric Statistical Methods
T Leonard and J.S.J Hsu, Bayesian Methods     

Measuring agreement between two statistics with applications to age standardization

Summary.  We detail a general method for measuring agreement between two statistics. An application is two ratios of directly standardized rates which differ only by the choice of the standard. If the statistics have a high value for the coefficient of agreement then the expected squared difference between the statistics is small relative to the variance of the average of the two statistics, and inferences vary little by changing statistics. The estimation of a coefficient of agreement between two statistics is not straightforward because there is only one pair of observed values, each statistic calculated from the data. We introduce estimators of the coefficient of agreement for two statistics and discuss their use, especially as applied to functions of standardized rates.     

林业投资均衡性的理论分析

我国林业投资体制的改革面临着许多新的课题,研究市场经济条件下林业投资状况十分必要。首先,从均衡理论出发,在分析宏观经济政策对林业的影响和林业投资现状基础上,对形成这种状况的主要原因进行了探讨;其次,以林业投资对象为主体,对其微观经济行为的成本与收益问题进行了分析;最后,提出深化林业投资体制改革要促进投资主体多元化,给予林业更多的政策扶持,进一步完善林业法律法规,以促进我国林业持续稳定地发展。     

影响消费者购买决策的产品属性效用实证分析

本文采用UTASTAR方法,以手机产品为例,从多属性效用这个新视角来评估决策这一因素,研究了七种手机产品对消费者的总效用、各属性的边际效用及各影响因素的重要程度.目前,产品属性影响力大小的获知主要依靠市场调研或网络调研,该文所采用的UTASTAR方法是市场调研方法的补充和完善.论文首先阐述了UTASTAR方法在研究消费者购买决策方面的优势,然后对通过网络挖掘获取的手机属性数据进行定量计算,结果各款手机的总效用排序与实际调查得到的消费者的关注度基本一致.另外,本文计算得到的属性边际效用和属性的权重,能够使营销人员和产品设计人员更好地把握手机产品的重要属性及其重要程度,有利于指导企业制定具有竞争力的产品和市场战略.     

新时期仍需坚持“效率优先,兼顾公平“的原则

改革开放以来 ,我国在分配制度上一直坚持“效率优先 ,兼顾公平”的原则 ,坚持这一原则 ,是社会主义本质的要求 ,是符合我国社会主义初级阶段国情的。世纪之初 ,在贯彻实施“十五”计划的新时期 ,我们仍需继续坚持这一分配原则。     

影响私人轿车拥有的因素分析

轿车进入家庭不是单一因素的结果。通过对影响轿车拥有和使用的因素,即效用、收入、城市结构、城市交通、城市道路设施和政策导向等六个方面的分析,指出应从多层面来研究中国汽车工业作为支柱产业的支撑条件,使汽车工业产出与运行需求均衡发展。