the present study is an attempt toward evaluating the performance of portfolios and asset selectionusing cross-efficiency evaluation. cross-efficiency evaluation is an effective way of ranking decisionmaking units (dmus) in data envelopment analysis (dea). conventional dea models assume nonnegativevalues for inputs and outputs. however, we know that unlike return and skewness, varianceis the on...

in some data envelopment analysis (dea) applications, some inputs of dmus have negative values with positive cost. this paper generalizes the global cost malmquist productivity index to compare the productivity of dierent dmus with negative inputs in any two periods of times under variable returns to scale (vrs) technology, and then the generalized index is decomposed to several components. th...

Data Envelopment Analysis (DEA) is a powerful performance measurement in economic sector and operations research to assess the relative efficiency for each decision making unit (DMU). In general, there are two assumptions in DEA. Firstly, the DEA assumes that all DMUs are homogenous in their environments and secondly, the DEA is a deterministic approach which refers to not allow to noise or err...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently DEA has been extended to examine the efficiency of two-stage processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model provides not only an overall efficiency score for the ent...

Airport efficiency is an area of increasing interest to academics, policy makers and practitioners. This has resulted in a body of literature applying various econometric techniques to compare efficiency between different samples of airports. This paper uses the multi-criteria decision making method Analytic Hierarchy Process (AHP) to incorporate the weightings of input and output variables int...

in the classical data envelopment analysis (dea) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. in recent years, there are few researches on handling missing data. this paper suggests a new interval based approach to apply missing data, which is the modified version of kousmanen (2009) approach. first, the prop...

This paper considers the relationship between Tolerance sensitivity analysis in optimization and metric sensitivity analysis in Data Envelopment Analysis (DEA). Herein, we extend the results on the generalized Tolerance framework proposed by Wendell and Chen and show how this framework includes DEA metric sensitivity as a special case. Further, we note how recent results in Tolerance sensitivit...

This paper presents aggregated cost efficiency scores for a balanced panel of British and Irish credit institutions and relates these scores to loan loss reserves as a first step in investigating their usefulness as possible indicators of financial fragility. The efficiency scores are obtained using the two most popular methods of efficiency measurement – data envelopment analysis (DEA) and the...

Keywords: Data envelopment analysis Target unit Undesirable output Multiple objective linear programming Minimax method a b s t r a c t Data Envelopment Analysis (DEA) is a mathematical programming technique for identifying efficient Decision Making Units (DMUs) with multiple inputs and multiple outputs. DEA provides a technical efficiency score for each DMU, a technical efficiency reference se...

Extended Data Envelopment Analysis/Discriminant Analysis (DEA/DA), is a widely applied approach for firstly the classification of observations into groups and secondly the prediction of the membership of newly examined observations. However in real-life problems we usually face observations that are vague or can’t be precisely measured. To provide an analyst for dealing with such data, we have ...