Abstract Data envelopment analysis (DEA), a useful assessment tool, has been used to solve the problem of preference voting and aggregation which require the determination of the weights associated with different ranking places. Instead of applying the same externally imposed weighting scheme to all candidates, DEA models allow each candidate to choose his/her own weights in order to maximize h...

in recent years, the existing competitions between investment companies have been increased largely by entering private investors in capital market. large and powerful companies try to achieve the goals predicted to increase the competition capacity. to analyze the efficiency of investment companies, parametric and non-parametric methods are used. in this research, based on the dissociation pow...

We set up a rich bilateral bargaining model with four salient points (disagreement point, ideal point, reference point, and tempered aspirations point), where the disagreement point and the utility possibilities frontier are endogenously determined. This model allows us to compare two bargaining solutions that use reference points, the Gupta-Livne solution and the tempered aspirations solution,...

This work presents a global geometric similarity scheme (GGSS) for feature selection in fault diagnosis, which is composed of global geometric model and similarity metric. The global geometric model is formed to construct connections between disjoint clusters in fault diagnosis. The similarity metric of the global geometric model is applied to filter feature subsets. To evaluate the performance...

Using super-efficiency, with regard to ranking efficient units, is increasing in DEA. However, this model has some problems such as the infeasibility. Thus, this article studies infeasibility of the input-based super-efficiency model (because of the zero inputs and outputs), and presents a solution by adding two virtual DMUs with mean values (one for inputs and one for outputs). Adding virtual ...

In the conventional data envelopment analysis (DEA) internal sub-processes of the production units are ignored. The current paper develops a network-DEA super-efficiency model to compare the performance of efficient network systems. A new ranking method is developed by aggregating the computed super-efficiency scores with a J-divergence measure. The proposed approach is then applied to evaluate...

University rankings generally present users with the problem of placing the results given for an institution in context. Only a comparison with the performance of all other institutions makes it possible to say exactly where an institution stands. In order to interpret the results of the SCImago Institutions Ranking (based on Scopus data) and the Leiden Ranking (based on Web of Science data), i...

DEA Classic models cannot be used for inaccurate and indeterminate data, and it is supposed that the data for all inputs and outputs are accurate and determinate. However, in real life situations uncertainty is more common. This article attempts to get the common weights for Decision-Making Units by developing DEA multi-objective models in the grey environment. First, we compute the privilege o...

In one dimension, there is essentially just one binomially distributed statistic, bias or correlation, for testing correctness of a key bit in Matsui’s Algorithm 1. In multiple dimensions, different statistical approaches for finding the correct key candidate are available. The purpose of this work is to investigate the efficiency of such test in theory and practice, and propose a new key class...

We propose a new model for rank aggregation from pairwise comparisons that captures both ranking heterogeneity across users and ranking inconsistency for each user. We establish a formal statistical equivalence between the new model and topic models. We leverage recent advances in the topic modeling literature to develop an algorithm that can learn shared latent rankings with provable statistic...