A Mechanism Design Approach to Ranking Asymmetric Auctions

I propose a new mechanism design approach to the problem of ranking standard auctions with two heterogeneous bidders. A key feature of the approach is that it may be possible to rank two auctions even if neither dominates the other for all combinations of types. The approach simplifies the analysis and unifies results in the existing literature. Roughly speaking, the first‐price auction is more profitable than the second‐price auction when the strong bidder's distribution is flatter and more disperse than the weak bidder's distribution. Applications include auctions with one‐sided externalities. Moreover, contrary to previous work, reserve prices are easily handled. Finally, the method can be extended to some environments with many bidders.     

时序多属性决策的广义等级偏好优序法

实际中很多复杂的多属性决策问题往往需要考虑多个时间序列的决策信息,针对这类时序多属性决策问题,本文在优序法的基础上,定义了广义优序数的概念。同时提出基于理想时间权向量的方法来解决时序多属性决策中时间权重的确定问题,进而在此基础上提出时序多属性决策的广义等级偏好优序法。最后,利用文献中的数据,用本文的方法对某集团的四个铜矿企业的综合效益进行了评价,其结果表明了本文方法的可行性和有效性。本文为解决时序多属性决策问题提供了一种新的方法和思路。     

A Parametric Test of Perfect Ranking in Balanced Ranked Set Sampling

Many techniques based on data which are drawn by Ranked Set Sampling (RSS) scheme assume that the ranking of observations is perfect. Therefore it is essential to develop some methods for testing this assumption. In this article, we propose a parametric location-scale free test for assessing the assumption of perfect ranking. The results of a simulation study in two special cases of normal and exponential distributions indicate that the proposed test performs well in comparison with its leading competitors.     

一种具有不确定偏好序信息的多指标群决策方法

针对具有不确定偏好序信息的多指标群决策问题,给出了一种决策分析方法。在本文中,首先对具有不确定偏好序信息的多指标群决策问题进行了描述;然后给出了将不确定偏好序转换为投票数的计算公式;进一步地,依据Bernardo方法的基本思想,根据每个专家给出的不确定偏好序信息,计算相应的投票数并构建群体投票矩阵,并基于群体投票矩阵构建0-1整数规划模型,通过求解模型可得到方案排序结果。最后,通过一个算例以及与已有方法的对比分析说明了本文给出的方法的可行性和有效性。     

The lean improvement of the chemical emissions of motor vehicles based on preference ranking: A PROMETHEE uncertainty analysis

The motor vehicle has provided mobility and individual freedom for millions of people. However, vehicles embody the dilemma of contemporary industrialisation in that the environmental costs of automobility are equally large. This non-country specific study undertakes a PROMETHEE-based preference ranking of a small set of motor vehicles based on constituents of their exhaust emissions. As a model of an interested party's preference ranking of the motor vehicles, the subsequent uncertainty (sensitivity) analysis considered here, relates to what minimal (lean) changes would be necessary to a vehicle's emissions so that their preference ranking is improved. For a particular manufacturer, it can identify the necessary engineering performance modifications to be made to improve their perceived consumer based ranking. This is compounded by a further consideration of different levels of importance conferred on the criteria (vehicle emissions) and analogous analyses undertaken. The visual elucidation of the results rankings and changes to criteria values, offers a clear presentation of the findings to the interested parties.     

组合评价方法在银行信用风险评价中的应用

针对贷款客户评价选择问题,在对已有研究方法分析的基础上,提出了以熵权与偏好顺序结构评估法相结合的排序方法。以借贷人的财务状况、借贷人的管理水平、借贷人的行业地位、借贷人资产的变现性和借贷人历史信用状况为评价准则,利用信息熵来确定评价准则的权重,用偏好顺序评价法确定贷款客户的贷款顺序,并进行了灵敏度分析。最后通过算例证明了该方法的有效性。     

Stochastic equivalence of ranking methods

The asymptotic behavior of linear rank statistics for comparing the locations of two populations, where the observations are ranked jointly with other populations, is considered. Under certain conditions, the asymptotic behavior of these statistics does not depend on which other populations are included in the ranking. In particular, the difference of a pair of these statistics, with the same score function, but based on two different rankings, converges to zero in probability under Pitman alternatives and Chernoff-Savage conditions on the scores and underlying distributions.     

Inference Based on General Linear Models for Order Restricted Randomization

This article develops statistical inference for the general linear models in order restricted randomized (ORR) designs. The ORR designs use the heterogeneity among experimental units to induce a negative correlation structure among responses obtained from different treatment regimes. This negative correlation structure acts as a variance reduction technique for treatment contrast. The parameters of the general linear models are estimated and a generalized F-test is constructed for its components. It is shown that the null distribution of the test statistic can be approximated reasonably well with an F-distribution for moderate sample sizes. It is also shown that the empirical power of the proposed test is substantially higher than the powers of its competitors in the literature. The proposed test and estimator are applied to a data set from a clinical trial to illustrate how one can improve such an experiment.     

Nonparametric Prediction Intervals Based on Ranked Set Samples

In this article, we develop nonparametric prediction intervals based on generalized ranked set samples using conditional as well as unconditional approaches. The predictions are developed for order statistics from a future sample as well as for order statistics from a future balanced ranked set sample. The effects of ranking errors on the coverage probabilities of these prediction intervals are also examined.