国际油气勘探开发项目风险研究——基于变权理论

国际油气勘探开发项目从资源丰度到技术难度、从政治形势到经济效益,都蕴含了诸多的风险,但已有的研究中通常使用常权评价模型,从而降低了项目的总体风险,隐没了突出问题的严重性,从而失去了评价的公正性和有效性。本文结合变权理论,构建了多层次、多目标的变权综合评价模型,计算项目风险的变权综合评价值。其中,通过对评价值上限和下限的引用,对关键风险指标设置了＂门槛＂条件,凸显了关键风险因素对决策的决定意义;而α取值的确定,也反映了决策者的目标取向。最后,本文通过项目案例验证了变权综合评价方法在国际油气合作项目风险评价中应用的有效性。     

基于一种组合赋权方法的物流园区选址研究

以物流园区的区位选择为研究背景,给出了一种权重确定模型,既照顾到决策者的主观偏好和决策的客观真实性,又充分利用各方案评价指标值的数量特征,从而确定指标的权重,得出各备选区位的优选排序。作者给出了模型的建立和求解过程,最后运行实例分析了该方法的有效性。     

变权公式中参数α的性态分析

本文运用公平理论模式分析了决策者在决策指标变化时的心理反应状况对变权公式中参数α取值的影响,并给出了两者之间的量化关系式。     

Calibration Weighting Methods for Complex Surveys

This paper presents a careful investigation of the three popular calibration weighting methods: (i) generalised regression; (ii) generalised exponential tilting and (iii) generalised pseudo empirical likelihood, with a major focus on computational aspects of the methods and some empirical evidences on calibrated weights. We also propose a simple weight trimming method for range‐restricted calibration. The finite sample behaviour of the weights obtained by the three calibration weighting methods and the effectiveness of the proposed weight trimming method are examined through limited simulation studies.     

A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP

We derive forecast weights and uncertainty measures for assessing the roles of individual series in a dynamic factor model (DFM) for forecasting the euro area GDP from monthly indicators. The use of the Kalman smoother allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information for the GDP forecasts beyond the monthly real activity measures. However, this is discovered only if their more timely publication is taken into account properly. Differences in publication lags play a very important role and should be considered in forecast evaluation.     

2SLS and OLS in a spatial autoregressive model with equal spatial weights

The paper considers a Cliff–Ord type spatial model with a spatially lagged dependent variable and a row normalized weighting matrix with equal weights. We show that the 2SLS and OLS estimators are inconsistent unless panel data are available. The weighting matrix in question is one which would naturally be considered if all units are neighbors to each other, and there is no other reasonable or observable measure of distance between them.     

基于PCA的小波多分辨图像融合方法

提出小渡多分辨率下主成份分析（PCA）的图像融合方法。首先利用小波变换对融合图像进行多分辨率分解，然后利用主成份分析方法确定图像小波低频近似系数的洎适应融合权重，采用局部区域“能量”法进行小渡高频细节系数的融合，最后将小波融合系数逆变换实现图像的融合处理。实验结果证实融合图像的目标特征突出，易于目视解译。     

群体成员位次排序研究——基于公共选择的视角

很多现实问题可以模型化为依据一定的评价指标体系而对群体成员进行住次排序。群体成员对位次排序评价指标权重的选择是对评价规则的选择,理性的群体成员必然会依据其掌握的信息对规则的选择进行计算,群体成员位次排序的结果依赖于群体成员的策略选择和群体成员的权力结构。文章建立了不同信息情况下群体成员的计算模型,给出了相应的算例,初步提出了分析结论和改进措施。     

Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers

Data envelopment analysis (DEA) has recently become relatively popular with road safety experts. Therefore, various decision-making units (DMUs), such as EU countries, have been assessed in terms of road safety performance (RSP). However, the DEA has been criticized because it evaluates DMUs based only on the concept of self-assessment, and, therefore does not provide a unique ranking for DMUs. Therefore, cross efficiency method (CEM) was developed to overcome this shortcoming. Peer-evaluations in addition to self-evaluation have made the CEM to be recognized as an effective method for ranking DMUs. The traditional CEM is based only on the standard CCR (Charnes, Cooper and Rhodes) model, and it evaluates DMUs according to their position relative to the best practice frontier while neglecting the worst practice frontier. However, the DMUs can also be assessed based on their position relative to the worst practice frontier. In this regard, the present study aims to provide a double-frontier CEM for assessing RSP by taking into account the best and worst frontiers simultaneously. For this purpose, the cross efficiency and cross anti-efficiency matrices are generated.Even though a weighted average method (WAM) is most frequently used for cross efficiency aggregation, the decision maker's (DM) preference structure may not be reflected. For this reason, the present study mainly focuses on the evidential reasoning approach (ERA), as a nonlinear aggregation method, rather than the linear WAM. Equal weights are often used for cross efficiency aggregation; consequently, the effect of the DM's subjective judgments in obtaining the overall efficiency is ignored. In this respect, the minimax entropy approach (MEA) and the maximum disparity approach (MMDA) are applied for determining the ordered weighted averaging (OWA) operator weights for cross efficiency aggregation. The weighted cross efficiencies and cross anti-efficiencies are then aggregated using the ERA. Finally, the proposed method, called DF-CEM-ERA, is used to evaluate the RSP of EU countries as well as Serbian police departments (PDs).