Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis (eBook)

CONTENTS 6
Chapter 1 DATA IRREGULARITIES AND STRUCTURAL COMPLEXITIES IN DEA 8
1. INTRODUCTION 8
2. DEA MODELS 9
3. DATA AND STRUCTURE ISSUES 14
REFERENCES 18
Chapter 2 RANK ORDER DATA IN DEA 19
1. INTRODUCTION 19
2. ORDINAL DATA IN R& D PROJECT SELECTION
3. MODELING LIKERT SCALE DATA: CONTINUOUS PROJECTION 22
4. THE CONTINUOUS PROJECTION MODEL AND IDEA 33
5. DISCRETE PROJECTION FOR LIKERT SCALE DATA: AN ADDITIVE MODEL 35
6. CONCLUSIONS 39
REFERENCES 39
Chapter 3 INTERVAL AND ORDINAL DATA 41
How Standard Linear DEA Model Treats Imprecise Data 41
1. INTRODUCTION 42
2. IMPRECISE DATA DATA DATA 43
3. MULTIPLIER IDEA (MIDEA): STANDARD DEA MODEL APPROACH 46
3.1 Converting the bounded data into a set of exact data 48
3.2 Converting the weak ordinal data into a set of exact data 49
3.3 Numerical Illustration 50
3.4 Application 52
3.5 Converting the strong ordinal data and ratio bounded data into a set of exact data 56
4. TREATMENT OF WEIGHT RESTRICTIONS 58
5. ENVELOPMENT IDEA (EIDEA) 63
6. CONCLUSIONS 65
REFERENCES 67
Chapter 4 VARIABLES WITH NEGATIVE VALUES IN DEA 69
1. INTRODUCTION 69
2. THE CLASSICAL APPROACH: THE TRANSLATION INVARIANT DEA MODELS 71
3. INTERVAL SCALE VARIABLES WITH NEGATIVE DATA AS A RESULT OF THE SUBTRACTION OF TWO RATIO SCALE VARIABLES 76
4. AVOIDING EFFICIENT UNITS WITH NEGATIVE OUTPUTS 78
5. THE DIRECTIONAL DISTANCE APPROACH 80
5.1 Efficiency measurement 80
5.2 Target setting 81
6. EFFICIENCY MEASUREMENT AND TARGET SETTING BY MEANS OF WEIGHTED ADDITIVE MODELS 82
6.1 Efficiency measurement 82
6.2 Target setting 83
7. ILLUSTRATIVE EXAMPLE 85
8. CONCLUSIONS 88
REFERENCES 88
Chapter 5 NON- DISCRETIONARY INPUTS 91
1. INTRODUCTION 91
2. PRODUCTION WITH NON-DISCRETIONARY INPUTS 93
3. THE BANKER AND MOREY MODEL 96
3.1 Input-Oriented Model 96
3.2 Illustrative Example Using Simulated Data 98
4. ALTERNATIVE DEA MODELS 101
4.1 Two-Stage Model Using DEA and Regression 101
4.2 Restricting Weights 102
4.3 Simulation Analysis 103
5. CONCLUSIONS 105
REFERENCES 106
Chapter 6 DEA WITH UNDESIRABLE FACTORS 108
1. INTRODUCTION 108
2. WEAK AND STRONG DISPOSABILITY OF UNDESIRABLE OUTPUTS 110
3. THE HYPERBOLIC OUTPUT EFFICIENCY MEASURE 111
4. A LINEAR TRANSFORMATION FOR UNDESIRABLE FACTORS 114
5. A DIRECTIONAL DISTANCE FUNCTION 115
6. NON-DISCRETIONARY INPUTS AND UNDESIRABLE OUTPUTS IN DEA 118
7. DISCUSSIONS AND CONCLUSION REMARKS 122
REFERENCES 125
Chapter 7 EUROPEAN NITRATE POLLUTION REGULATION AND FRENCH PIG FARMS’ PERFORMANCE 127
1. INTRODUCTION 128
2. MODELLING TECHNOLOGIES WITH GOOD AND BAD OUTPUTS 130
3. MODELLING TECHNOLOGIES WITH AN ENVIRONMENTAL STANDARD ON THE BY- OUTPUT 134
4. DATA AND EMPIRICAL MODEL 136
5. RESULTS 138
6. CONCLUSION 140
REFERENCES 141
ACKNOWLEDGEMENTS 142
Chapter 8 PCA- DEA 143
Reducing the curse of dimensionality 143
1. INTRODUCTION 143
2. DATA ENVELOPMENT ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS 144
3. THE PCA-DEA CONSTRAINED MODEL FORMULATION 146
3.1 PCA-DEA model 146
3.2 PCA-DEA constrained model 149
4. APPLICATION OF THE PCA-DEA MODELS 151
5. SUMMARY AND CONCLUSIONS 154
REFERENCES 155
Chapter 9 MININGNONPARAMETRIC FRONTIERS 158
1. INTRODUCTION 158
2. THE DEA PARADIGM AND THE PRODUCTION POSSIBILITY SET 160
3. FRONTIER MINING 164
4. COMPUTATIONAL TESTS 170
5. CONCLUSIONS 172
REFERENCES 173
Chapter 10 DEA PRESENTED GRAPHICALLY USING MULTI- DIMENSIONAL SCALING 174
1. INTRODUCTION 174
2. CO-PLOT 176
3. CO-PLOT AND DEA 178
4. FINNISH FORESTRY BOARD ILLUSTRATION 180
5. CONCLUSIONS 187
REFERENCES 188
Chapter 11 DEA MODELS FOR SUPPLY CHAIN OR MULTI-STAGE STRUCTURE 191
1. INTRODUCTION 192
2. NOTIONS AND STANDARD DEA MODELS 193
3. ZHU (2003) APPROACH1 195
4. COOPERATIVE AND NON-COOPERATIVE APPROACHES 196
4.1 The Non-cooperative Model 196
4.2 The Cooperative Model 203
4.3 The Cooperative Model 207
5. CONCLUSIONS 208
REFERENCES 209
Chapter 12 NETWORK DEA 211
1. INTRODUCTION 212
2. STATIC NETWORK MODEL 213
3. DYNAMIC NETWORK MODEL 221
4. TECHNOLOGY ADOPTION 224
5. EPILOG 229
REFERENCES 230
Chapter 13 CONTEXT-DEPENDENT DATA ENVELOPMENT ANALYSIS AND ITS USE 243
1. INTRODUCTION 243
2. CONTEXT-DEPENDENT DATA ENVELOPMENT ANALYSIS 245
2.1 Stratification DEA Model 245
2.2 Attractiveness and Progress 247
2.3 Output oriented context-dependent DEA model 248
2.4 Context-dependent DEA with Value Judgment 249
3. SLACK-BASED CONTEXT-DEPENDENT DEA 252
4. APPLICATION 255
5. CONCLUDING REMARKS 260
REFERENCES 260
Chapter 14 FLEXIBLE MEASURES-CLASSIFYING INPUTS AND OUTPUTS 262
1. INTRODUCTION 262
2. IDENTIFYING THE INPUT OUTPUT STATUS OF FLEXIBLE MEASURES 263
3. APPLICATION 268
4. CONCLUSIONS 270
REFERENCES 271
Chapter 15 INTEGER DEA MODELS 272
How DEA models can handle integer inputs and outputs 272
1. INTRODUCTION 272
2. INTEGER RADIAL DEA MODELS 274
3. ILLUSTRATION OF INTEGER RADIAL DEA MODEL 279
4. OTHER INTEGER DEA MODELS 282
5. CONCLUSIONS 288
REFERENCES 289
Chapter 16 DATA ENVELOPMENT ANALYSIS WITH MISSING DATA 291
A Reliable Solution Method 291
1. INTRODUCTION 291
2. THE FUZZY SET APPROACH 293
3. A CASE ANALYSIS 297
4. A COMPARISON 300
5. CONCLUSION 302
REFERENCES 302
Chapter 17 PREPARING YOUR DATA FOR DEA 305
1. SELECTION OF INPUTS AND OUTPUTS AND NUMBER OF DMUS 305
2. REDUCING DATA SETS FOR INPUT/OUTPUT FACTORS THAT ARE CORRELATED 308
3. IMBALANCE IN DATA MAGNITUDES 310
4. NEGATIVE NUMBERS AND ZERO VALUES4 312
5. MISSING DATA 317
REFERENCES 318
ABOUT THE AUTHORS 321
Index 331