{"product_id":"advances-in-dea-theory-and-applications-isbn-9781118945629","title":"Advances in DEA Theory and Applications","description":"\u003cp\u003e\u003cb\u003eA key resource and framework for assessing the performance of competing entities, including forecasting models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAdvances in DEA Theory and Applications \u003c\/i\u003eprovides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting.\u003c\/p\u003e \u003cp\u003eDesigned for use by anyone involved in research in the field of forecasting or in another application area where forecasting drives decision making, this text can be applied to a wide range of contexts, including education, health care, banking, armed forces, auditing, market research, retail outlets, organizational effectiveness, transportation, public housing, and manufacturing. This vital resource: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplores the latest developments in DEA frameworks for the performance evaluation of entities such as public or private organizational branches or departments, economic sectors, technologies, and stocks\u003c\/li\u003e \u003cli\u003ePresents a novel area of application for DEA; namely, the performance evaluation of forecasting models\u003c\/li\u003e \u003cli\u003ePromotes the use of DEA to assess the performance of forecasting models in a wide area of applications\u003c\/li\u003e \u003cli\u003eProvides rich, detailed examples and case studies\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAdvances in DEA Theory and Applications \u003c\/i\u003eincludes information on a balanced benchmarking tool that is designed to help organizations examine their assumptions about their productivity and performance.\u003c\/p\u003e \u003cp\u003eList of Contributors xx\u003c\/p\u003e \u003cp\u003eAbout the Authors xxii\u003c\/p\u003e \u003cp\u003ePreface xxxii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I DEA Theory 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Radial DEA Models 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Basic Data 3\u003c\/p\u003e \u003cp\u003e1.3 Input-Oriented CCR Model 4\u003c\/p\u003e \u003cp\u003e1.4 The Input-Oriented BCC Model 6\u003c\/p\u003e \u003cp\u003e1.5 The Output-Oriented Model 7\u003c\/p\u003e \u003cp\u003e1.6 Assurance Region Method 8\u003c\/p\u003e \u003cp\u003e1.7 The Assumptions Behind Radial Models 8\u003c\/p\u003e \u003cp\u003e1.8 A Sample Radial Model 8\u003c\/p\u003e \u003cp\u003eReferences 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Non-Radial DEA Models 11\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 11\u003c\/p\u003e \u003cp\u003e2.2 The SBM Model 12\u003c\/p\u003e \u003cp\u003e2.3 An Example of an SBM Model 15\u003c\/p\u003e \u003cp\u003e2.4 The Dual Program of the SBM Model 17\u003c\/p\u003e \u003cp\u003e2.5 Extensions of the SBM Model 17\u003c\/p\u003e \u003cp\u003e2.6 Concluding Remarks 18\u003c\/p\u003e \u003cp\u003eReferences 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Directional Distance DEA Models 20\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHirofumi Fukuyama and William L. Weber\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 20\u003c\/p\u003e \u003cp\u003e3.2 Directional Distance Model 20\u003c\/p\u003e \u003cp\u003e3.3 Variable-Returns-to-Scale DD Models 23\u003c\/p\u003e \u003cp\u003e3.4 Slacks-Based DD Model 23\u003c\/p\u003e \u003cp\u003e3.5 Choice of Directional Vectors 25\u003c\/p\u003e \u003cp\u003eReferences 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Super-Efficiency DEA Models 28\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 28\u003c\/p\u003e \u003cp\u003e4.2 Radial Super-Efficiency Models 28\u003c\/p\u003e \u003cp\u003e4.3 Non-Radial Super-Efficiency Models 29\u003c\/p\u003e \u003cp\u003e4.4 An Example of a Super-Efficiency Model 31\u003c\/p\u003e \u003cp\u003eReferences 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Determining Returns to Scale in the VRS DEA Model 33\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBiresh K. Sahoo and Kaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 33\u003c\/p\u003e \u003cp\u003e5.2 Technology Specification and Scale Elasticity 34\u003c\/p\u003e \u003cp\u003e5.3 Summary 37\u003c\/p\u003e \u003cp\u003eReferences 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Malmquist Productivity Index Models 40\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone and Miki Tsutsui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 40\u003c\/p\u003e \u003cp\u003e6.2 Radial Malmquist Model 43\u003c\/p\u003e \u003cp\u003e6.3 Non-Radial and Oriented Malmquist Model 45\u003c\/p\u003e \u003cp\u003e6.4 Non-Radial and Non-Oriented Malmquist Model 47\u003c\/p\u003e \u003cp\u003e6.5 Cumulative Malmquist Index (CMI) 48\u003c\/p\u003e \u003cp\u003e6.6 Adjusted Malmquist Index (AMI) 49\u003c\/p\u003e \u003cp\u003e6.7 Numerical Example 50\u003c\/p\u003e \u003cp\u003e6.8 Concluding Remarks 55\u003c\/p\u003e \u003cp\u003eReferences 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Network DEA Model 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone and Miki Tsutsui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 57\u003c\/p\u003e \u003cp\u003e7.2 Notation and Production Possibility Set 58\u003c\/p\u003e \u003cp\u003e7.3 Description of Network Structure 59\u003c\/p\u003e \u003cp\u003e7.4 Objective Functions and Efficiencies 61\u003c\/p\u003e \u003cp\u003eReference 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Dynamic DEA Model 64\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone and Miki Tsutsui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 64\u003c\/p\u003e \u003cp\u003e8.2 Notation and Production Possibility Set 65\u003c\/p\u003e \u003cp\u003e8.3 Description of Dynamic Structure 67\u003c\/p\u003e \u003cp\u003e8.4 Objective Functions and Efficiencies 69\u003c\/p\u003e \u003cp\u003e8.5 Dynamic Malmquist Index 71\u003c\/p\u003e \u003cp\u003eReferences 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 The Dynamic Network DEA Model 74\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone and Miki Tsutsui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 74\u003c\/p\u003e \u003cp\u003e9.2 Notation and Production Possibility Set 75\u003c\/p\u003e \u003cp\u003e9.3 Description of Dynamic Network Structure 77\u003c\/p\u003e \u003cp\u003e9.4 Objective Function and Efficiencies 80\u003c\/p\u003e \u003cp\u003e9.5 Dynamic Divisional Malmquist Index 82\u003c\/p\u003e \u003cp\u003eReferences 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Stochastic DEA: The Regression-Based Approach 85\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAndrew L. Johnson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 85\u003c\/p\u003e \u003cp\u003e10.2 Review of Literature on Stochastic DEA 87\u003c\/p\u003e \u003cp\u003e10.3 Conclusions 96\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A Comparative Study of AHP and DEA 100\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 100\u003c\/p\u003e \u003cp\u003e11.2 A Glimpse of Data Envelopment Analysis 100\u003c\/p\u003e \u003cp\u003e11.3 Benefit\/Cost Analysis by Analytic Hierarchy Process 102\u003c\/p\u003e \u003cp\u003e11.4 Efficiencies in AHP and DEA 104\u003c\/p\u003e \u003cp\u003e11.5 Concluding Remarks 105\u003c\/p\u003e \u003cp\u003eReferences 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Computational Method for Solving DEA Problems with Infinitely Many DMUs 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbraham Charnes and Kaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 107\u003c\/p\u003e \u003cp\u003e12.2 Problem 108\u003c\/p\u003e \u003cp\u003e12.3 Outline of the Method 109\u003c\/p\u003e \u003cp\u003e12.4 Details of the Method When Z is One-Dimensional 110\u003c\/p\u003e \u003cp\u003e12.5 General Case 113\u003c\/p\u003e \u003cp\u003e12.6 Concluding Remarks (by Tone) 115\u003c\/p\u003e \u003cp\u003eAppendix 12.A Proof of Theorem 12.1 115\u003c\/p\u003e \u003cp\u003eAppendix 12.B Proof of Theorem 12.2 116\u003c\/p\u003e \u003cp\u003eReference 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Dea Applications (past–present Scenario) 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Examining the Productive Performance of Life Insurance Corporation of India 119\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone and Biresh K. Sahoo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 119\u003c\/p\u003e \u003cp\u003e13.2 Nonparametric Approach to Measuring Scale Elasticity 121\u003c\/p\u003e \u003cp\u003e13.3 The Dataset for LIC Operations 128\u003c\/p\u003e \u003cp\u003e13.4 Results and Discussion 130\u003c\/p\u003e \u003cp\u003e13.5 Concluding Remarks 136\u003c\/p\u003e \u003cp\u003eReferences 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 An Account of DEA-Based Contributions in the Banking Sector 141\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJamal Ouenniche, Skarleth Carrales, Kaoru Tone and Hirofumi Fukuyama\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 141\u003c\/p\u003e \u003cp\u003e14.2 Performance Evaluation of Banks: A Detailed Account 142\u003c\/p\u003e \u003cp\u003e14.3 Current State of the Art Summarized 154\u003c\/p\u003e \u003cp\u003e14.4 Conclusion 163\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 DEA in the Healthcare Sector 172\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHiroyuki Kawaguchi, Kaoru Tone and Miki Tsutsui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 172\u003c\/p\u003e \u003cp\u003e15.2 Method and Data 174\u003c\/p\u003e \u003cp\u003e15.3 Results 184\u003c\/p\u003e \u003cp\u003e15.4 Discussion 188\u003c\/p\u003e \u003cp\u003eAcknowledgements 189\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 DEA in the Transport Sector 192\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMing-Miin Yu and Li-Hsueh Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 192\u003c\/p\u003e \u003cp\u003e16.2 DNDEA in Transport 194\u003c\/p\u003e \u003cp\u003e16.3 Extension 200\u003c\/p\u003e \u003cp\u003e16.4 Application 207\u003c\/p\u003e \u003cp\u003e16.5 Conclusions 212\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Dynamic Network Efficiency of Japanese Prefectures 216\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHirofumi Fukuyama, Atsuo Hashimoto, Kaoru Tone and William L. Weber\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 216\u003c\/p\u003e \u003cp\u003e17.2 Multiperiod Dynamic Multiprocess Network 217\u003c\/p\u003e \u003cp\u003e17.3 Efficiency\/Productivity Measurement 221\u003c\/p\u003e \u003cp\u003e17.4 Empirical Application 222\u003c\/p\u003e \u003cp\u003e17.5 Conclusions 229\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 A Quantitative Analysis of Market Utilization in Electric Power Companies 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMiki Tsutsui and Kaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 231\u003c\/p\u003e \u003cp\u003e18.2 The Functions of the Trading Division 232\u003c\/p\u003e \u003cp\u003e18.3 Measuring the Effect of Energy Trading 235\u003c\/p\u003e \u003cp\u003e18.4 DEA Calculation 242\u003c\/p\u003e \u003cp\u003e18.5 Empirical Results 243\u003c\/p\u003e \u003cp\u003e18.6 Concluding Remarks 248\u003c\/p\u003e \u003cp\u003eReferences 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 DEA in Resource Allocation 250\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMing-Miin Yu and Li-Hsueh Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 250\u003c\/p\u003e \u003cp\u003e19.2 Centralized DEA in Resource Allocation 252\u003c\/p\u003e \u003cp\u003e19.3 Applications of Centralized DEA in Resource Allocation 261\u003c\/p\u003e \u003cp\u003e19.4 Extension 265\u003c\/p\u003e \u003cp\u003e19.5 Conclusions 268\u003c\/p\u003e \u003cp\u003eReferences 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 How to Deal with Non-convex Frontiers in Data Envelopment Analysis 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone and Miki Tsutsui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 271\u003c\/p\u003e \u003cp\u003e20.2 Global Formulation 273\u003c\/p\u003e \u003cp\u003e20.3 In-cluster Issue: Scale- and Cluster-Adjusted DEA Score 276\u003c\/p\u003e \u003cp\u003e20.4 An Illustrative Example 281\u003c\/p\u003e \u003cp\u003e20.5 The Radial-Model Case 284\u003c\/p\u003e \u003cp\u003e20.6 Scale-Dependent Dataset and Scale Elasticity 287\u003c\/p\u003e \u003cp\u003e20.7 Application to a Dataset Concerning Japanese National Universities 289\u003c\/p\u003e \u003cp\u003e20.8 Conclusions 294\u003c\/p\u003e \u003cp\u003eAppendix 20.A Clustering Using Returns to Scale and Scale Efficiency 295\u003c\/p\u003e \u003cp\u003eAppendix 20.B Proofs of Propositions 295\u003c\/p\u003e \u003cp\u003eReferences 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Using DEA to Analyze the Efficiency of Welfare Offices and Influencing Factors: The Case of Japan’s Municipal Public Assistance Programs 300\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMasayoshi Hayashi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 300\u003c\/p\u003e \u003cp\u003e21.2 Institutional Background, DEA, and Efficiency Scores 301\u003c\/p\u003e \u003cp\u003e21.3 External Effects on Efficiency 304\u003c\/p\u003e \u003cp\u003e21.4 Quantile Regression Analysis 309\u003c\/p\u003e \u003cp\u003e21.5 Concluding Remarks 312\u003c\/p\u003e \u003cp\u003eAcknowledgements 312\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 DEA as a Kaizen Tool: SBM Variations Revisited 315\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 315\u003c\/p\u003e \u003cp\u003e22.2 The SBM-Min Model 316\u003c\/p\u003e \u003cp\u003e22.3 The SBM-Max Model 318\u003c\/p\u003e \u003cp\u003e22.4 Observations 321\u003c\/p\u003e \u003cp\u003e22.5 Numerical Examples 323\u003c\/p\u003e \u003cp\u003e22.6 Conclusions 330\u003c\/p\u003e \u003cp\u003eReferences 330\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Dea for Forecasting and Decision-making (past–present–future Scenario) 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Corporate Failure Analysis Using SBM 333\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJoseph C. Paradi, Xiaopeng Yang and Kaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 333\u003c\/p\u003e \u003cp\u003e23.2 Literature Review 334\u003c\/p\u003e \u003cp\u003e23.3 Methodology 340\u003c\/p\u003e \u003cp\u003e23.4 Application to Bankruptcy Prediction 343\u003c\/p\u003e \u003cp\u003e23.5 Conclusions 352\u003c\/p\u003e \u003cp\u003eReferences 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Ranking of Bankruptcy Prediction Models under Multiple Criteria 357\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJamal Ouenniche, Mohammad M. Mousavi, Bing Xu and Kaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 357\u003c\/p\u003e \u003cp\u003e24.2 An Overview of Bankruptcy Prediction Models 359\u003c\/p\u003e \u003cp\u003e24.3 A Slacks-Based Super-Efficiency Framework for Assessing Bankruptcy Prediction Models 366\u003c\/p\u003e \u003cp\u003e24.4 Empirical Results from Super-Efficiency DEA 372\u003c\/p\u003e \u003cp\u003e24.5 Conclusion 376\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 DEA in Performance Evaluation of Crude Oil Prediction Models 381\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJamal Ouenniche, Bing Xu and Kaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 381\u003c\/p\u003e \u003cp\u003e25.2 An Overview of Crude Oil Prices and Their Volatilities 385\u003c\/p\u003e \u003cp\u003e25.3 Assessment of Prediction Models of Crude Oil Price Volatility 388\u003c\/p\u003e \u003cp\u003e25.4 Conclusion 401\u003c\/p\u003e \u003cp\u003eReferences 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Predictive Efficiency Analysis: A Study of US Hospitals 404\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAndrew L. Johnson and Chia-Yen Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 404\u003c\/p\u003e \u003cp\u003e26.2 Modeling of Predictive Efficiency 405\u003c\/p\u003e \u003cp\u003e26.3 Study of US Hospitals 408\u003c\/p\u003e \u003cp\u003e26.4 Forecasting, Benchmarking, and Frontier Shifting 412\u003c\/p\u003e \u003cp\u003e26.5 Conclusions 416\u003c\/p\u003e \u003cp\u003eReferences 417\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Efficiency Prediction Using Fuzzy Piecewise Autoregression 419\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMing-Miin Yu and Bo Hsiao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 419\u003c\/p\u003e \u003cp\u003e27.2 Efficiency Prediction 420\u003c\/p\u003e \u003cp\u003e27.3 Modeling and Formulation 423\u003c\/p\u003e \u003cp\u003e27.4 Illustrating the Application 433\u003c\/p\u003e \u003cp\u003e27.5 Discussion 438\u003c\/p\u003e \u003cp\u003e27.6 Conclusion 440\u003c\/p\u003e \u003cp\u003eReferences 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Time Series Benchmarking Analysis for New Product Scheduling: Who Are the Competitors and How Fast Are They Moving Forward? 443\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDong-Joon Lim and Timothy R. Anderson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 443\u003c\/p\u003e \u003cp\u003e28.2 Methodology 445\u003c\/p\u003e \u003cp\u003e28.3 Application: Commercial Airplane Development 449\u003c\/p\u003e \u003cp\u003e28.4 Conclusion and Matters for Future Work 454\u003c\/p\u003e \u003cp\u003eReferences 455\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 DEA Score Confidence Intervals with Past–Present and Past–Present–Future-Based Resampling 459\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone and Jamal Ouenniche\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 459\u003c\/p\u003e \u003cp\u003e29.2 Proposed Methodology 461\u003c\/p\u003e \u003cp\u003e29.3 An Application to Healthcare 465\u003c\/p\u003e \u003cp\u003e29.4 Conclusion 476\u003c\/p\u003e \u003cp\u003eReferences 478\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 DEA Models Incorporating Uncertain Future Performance 480\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTsung-Sheng Chang, Kaoru Tone and Chen-Hui Wu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 480\u003c\/p\u003e \u003cp\u003e30.2 Generalized Dynamic Evaluation Structures 482\u003c\/p\u003e \u003cp\u003e30.3 Future Performance Forecasts 484\u003c\/p\u003e \u003cp\u003e30.4 Generalized Dynamic DEA Models 487\u003c\/p\u003e \u003cp\u003e30.5 Empirical Study 495\u003c\/p\u003e \u003cp\u003e30.6 Conclusions 513\u003c\/p\u003e \u003cp\u003eReferences 514\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Site Selection for the Next-Generation Supercomputing Center of Japan 516\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKaoru Tone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction 516\u003c\/p\u003e \u003cp\u003e31.2 Hierarchical Structure and Group Decision by AHP 519\u003c\/p\u003e \u003cp\u003e31.3 DEA Assurance Region Approach 521\u003c\/p\u003e \u003cp\u003e31.4 Application to the Site Selection Problem 522\u003c\/p\u003e \u003cp\u003e31.5 Decision and Conclusion 527\u003c\/p\u003e \u003cp\u003eReferences 527\u003c\/p\u003e \u003cp\u003eAppendix A: Dea-solver-pro 529\u003c\/p\u003e \u003cp\u003eIndex 535\u003c\/p\u003e   \u003cp\u003e \u003cb\u003eKAORU TONE\u003c\/b\u003e is with the National Graduate Institute for Policy Studies, Japan. His contribution to DEA has a variety of attainments. He authored a classical book \u003ci\u003eData Envelopment Analysis:  A Comprehensive Text with Models, Applications, References and DEA-Solver Software\u003c\/i\u003e under the  co-authorship with Professor Cooper (University of Texas) and Professor Seiford (University of Michigan). He also published many papers on DEA in international journals. Kaoru Tone opened a new avenue for performance evaluation, called Slacks-based Measure (SBM) that is widely utilized over the world. His recent innovations include Network SBM, Dynamic SBM, Dynamic DEA with Network Structure, Congestion, Returns-to-growth in DEA, Ownership-specified Network DEA, Non-convex Frontier DEA, Past-Present-Future Inter-temporal DEA, Resampling DEA and SBM-Max.      \u003c\/p\u003e\u003cp\u003e \u003cb\u003eA key resource and framework for assessing the performance of competing entities, including forecasting models\u003c\/b\u003e   \u003c\/p\u003e\u003cp\u003e \u003ci\u003eAdvances in DEA Theory and Applications\u003c\/i\u003e provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting.   \u003c\/p\u003e\u003cp\u003e Designed for use by anyone involved in research in the field of forecasting or in another application area where forecasting drives decision making, this text can be applied to a wide range of contexts, including education, health care, banking, armed forces, auditing, market research, retail outlets, organizational effectiveness, transportation, public housing, and manufacturing. This vital resource:   \u003c\/p\u003e\u003cul\u003e \u003cli\u003eExplores the latest developments in DEA frameworks for the performance evaluation of entities  such as public or private organizational branches or departments, economic sectors, technologies, and stocks\u003c\/li\u003e \u003cli\u003ePresents a novel area of application for DEA; namely, the performance evaluation of forecasting models\u003c\/li\u003e \u003cli\u003ePromotes the use of DEA to assess the performance of forecasting models in a wide area  of applications\u003c\/li\u003e \u003cli\u003eProvides rich, detailed examples and case studies\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e \u003cp\u003e \u003ci\u003eAdvances in DEA Theory and Applications\u003c\/i\u003e includes information on a balanced benchmarking tool that is designed to help organizations examine their assumptions about their productivity and performance.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988676526309,"sku":"NP9781118945629","price":123.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118945629.jpg?v=1761781218","url":"https:\/\/k12savings.com\/products\/advances-in-dea-theory-and-applications-isbn-9781118945629","provider":"K12savings","version":"1.0","type":"link"}