An Extension of Cross Redundancy of Interval Scale Outputs and Inputs in DEA

It is well known that data envelopment analysis (DEA) models are sensitive to selection of input and output variables. As the number of variables increases, the ability to discriminate between the decision making units (DMUs) decreases. Thus, to preserve the discriminatory power of a DEA model, the number of inputs and outputs should be kept at a reasonable level. There are many cases in which an interval scale output in the sample is derived from the subtraction of nonnegative linear combination of ratio scale outputs and nonnegative linear combination of ratio scale inputs. There are also cases in which an interval scale input is derived from the subtraction of nonnegative linear combination of ratio scale inputs and nonnegative linear combination of ratio scale outputs. Lee and Choi (2010) called such interval scale output and input a cross redundancy. They proved that the addition or deletion of a cross-redundant output variable does not affect the efficiency estimates yielded by the CCR or BCC models. In this paper, we present an extension of cross redundancy of interval scale outputs and inputs in DEA models. We prove that the addition or deletion of a cross-redundant output and input variable does not affect the efficiency estimates yielded by the CCR or BCC models

An Alternative Approach to Reduce Dimensionality in Data Envelopment Analysis

Principal component analysis reduces dimensionality; however, uncorrelated components imply the existence of variables with weights of opposite signs. This complicates the application in data envelopment analysis. To overcome problems due to signs, a modification to the component axes is proposed and was verified using Monte Carlo simulations

Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet, there is a challenge to select the best Web service which can offer more quality-of-service (QoS) values at the lowest price. Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet. In this paper, we modify the interval data envelopment analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors. We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models. The experimental results show that the correlation between the proposed models and the interval DEA models is significant. Also, the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the proposed models for QoS-aware Web service composition. Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase. These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the marke

Production Efficiency versus Ownership: The Case of China

In this study, we explore the pattern of efficiency among enterprises in China‘s 29 provinces across different ownership types in heavy and light industries and across different regions (coastal, central and western). We do so by performing a bootstrap-based analysis of group efficiencies (weighted and non-weighted), estimating and comparing densities of efficiency distributions, and conducting a bootstrapped truncated regression analysis. We find evidence of interesting differences in efficiency levels among various ownership groups, especially for foreign and local ownership, which have different patterns for light and heavy industries.efficiency, data envelopment analysis, bootstrapping, ownership, China

Production Efficiency versus Ownership: The Case of China

In this study, we explore the pattern of efficiency among enterprises in China’s 29 provinces across different ownership types in heavy and light industries and across different regions (coastal, central and western). We do so by performing a bootstrap-based analysis of group efficiencies (weighted and non-weighted), estimating and comparing densities of efficiency distributions, and conducting a bootstrapped truncated regression analysis. We find evidence of interesting differences in efficiency levels among various ownership groups, especially for foreign and local ownership, which have different patterns for light and heavy industries.Efficiency; Data envelopment analysis; Bootstrapping; Ownership; China

Gap Minimization for Peer-Evaluation in DEA Cross-Efficiency

Cross-efficiency evaluation is an effective and widely used method for ranking decision making units (DMUs) in data envelopment analysis (DEA). Gap minimization criterion is introduced in aggressive and benevolent cross-efficiency methods to avoid possible extreme efficiency from peer-evaluation and to get equitable results. On the basis of this criterion, a weighted cross-efficiency method with similarity distance that, respectively, considers the aggressive and the benevolent formulations is proposed to determine cross-efficiency. The weights of the cross-evaluation determined by this method are positively influenced by self-evaluation and thus are propitious to resolving conflict. Numerical demonstration reveals the feasibility of the proposed method

An examination of the banking efficiency of the BRICS countries : a perspective derived from the oil price volatility

This study examines the influence of Oil Price Volatility on Banks efficiency within the BRICS countries (Brazil, Russia, India, China, and South Africa) noting the importance of the banking sector efficiency as a tool to ensure financial stability in the region. Being able to measure efficiency levels in banks determines how successful a bank is in managing its operations and achieving its goals. A sample data of 112 banks was selected using the Bank Scope database over the time interval 2003 to 2018 to inspect banking sector relative efficiency following a non-parametric methodology known as Data Envelopment Analysis (DEA). The paper applies a two-stage model to process the empirical results consisting in using the Data Envelopment Analysis (DEA) to identify the scores of banks efficiency at a first stage (Stage 1) and determining how volatility in Oil price has impact on these scores of efficiencies on a second stage (Stage 2). Findings of the study indicate that the Chinese banking system shows the highest efficiency (90%), followed by the South Africa (87%), followed by the Brazilian and Indian banking system with efficiency level of (77%), the Russian bank industry revealed the lowest efficient banking system with level of efficiency (50%)

Robustness analysis based on weight restrictions in data envelopment analysis

Includes bibliographical references.Evaluating the performance of organisations is essential to good planning and control. Part of this process is monitoring the performance of organisations against their goals. The comparative efficiency of organizations using common inputs and outputs makes it possible for organizations to improve their performance so that can operate as the most efficient organizations. Resources and outputs can be very diversified in nature and it is complex to assess organizations using such resources and outputs. Data Envelopment Analysis models are designed to facilitate this of assessment and aim to evaluate the relative efficiency of organisations. Chapter 2 is dedicated to the basic Data Envelopment Analysis. We present the following: * A review of the Data Envelopment Analysis models; * The properties and particularities of each model. In chapter 3, we present our literature survey on restrictions. Data Envelopment Analysis is a value-free frontier which has the of yielding more objective efficiency measures. However, the complete freedom in the determination of weights for the factors and products) relevant to the assessment of organisations has led to some problems such as: zero-weights and lack of discrimination between efficient organizations. Weight restriction methods were introduced in order to tackle these problems. The first part of chapter 3 in detail the motivations for weight restrictions while the second part presents the actual weight restriction rnethods