Zhu / Cook Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

1. Auflage 2007
ISBN: 978-0-387-71607-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 334 Seiten

In a relatively short period of time, data envelopment analysis (DEA) has grown into a powerful analytical tool for measuring and evaluating performance. DEA is computational at its core and this book is one of several Springer aim to publish on the subject. This work deals with the micro aspects of handling and modeling data issues in DEA problems. It is a handbook treatment dealing with specific data problems, including imprecise data and undesirable outputs.

Autoren/Hrsg.

Zhu, J.

Zhu, Joe

Cook, Wade D.

Inhaltsverzeichnis

1;CONTENTS;6
2;Chapter 1 DATA IRREGULARITIES AND STRUCTURAL COMPLEXITIES IN DEA;8
2.1;1. INTRODUCTION;8
2.2;2. DEA MODELS;9
2.3;3. DATA AND STRUCTURE ISSUES;14
2.4;REFERENCES;18
3;Chapter 2 RANK ORDER DATA IN DEA;19
3.1;1. INTRODUCTION;19
3.2;2. ORDINAL DATA IN R&D PROJECT SELECTION;21
3.3;3. MODELING LIKERT SCALE DATA: CONTINUOUS PROJECTION;22
3.4;4. THE CONTINUOUS PROJECTION MODEL AND IDEA;33
3.5;5. DISCRETE PROJECTION FOR LIKERT SCALE DATA: AN ADDITIVE MODEL ;35
3.6;6. CONCLUSIONS;39
3.7;REFERENCES;39
4;Chapter 3 INTERVAL AND ORDINAL DATA;41
4.1;How Standard Linear DEA Model Treats Imprecise Data;41
4.2;1. INTRODUCTION;42
4.3;2. IMPRECISE DATA DATA DATA;43
4.4;3. MULTIPLIER IDEA (MIDEA): STANDARD DEA MODEL APPROACH;46
4.4.1;3.1 Converting the bounded data into a set of exact data;48
4.4.2;3.2 Converting the weak ordinal data into a set of exact data;49
4.4.3;3.3 Numerical Illustration;50
4.4.4;3.4 Application;52
4.4.5;3.5 Converting the strong ordinal data and ratio bounded data into a set of exact data ;56
4.5;4. TREATMENT OF WEIGHT RESTRICTIONS;58
4.6;5. ENVELOPMENT IDEA (EIDEA);63
4.7;6. CONCLUSIONS;65
4.8;REFERENCES;67
5;Chapter 4 VARIABLES WITH NEGATIVE VALUES IN DEA;69
5.1;1. INTRODUCTION;69
5.2;2. THE CLASSICAL APPROACH: THE TRANSLATION INVARIANT DEA MODELS;71
5.3;3. INTERVAL SCALE VARIABLES WITH NEGATIVE DATA AS A RESULT OF THE SUBTRACTION OF TWO RATIO SCALE VARIABLES;76
5.4;4. AVOIDING EFFICIENT UNITS WITH NEGATIVE OUTPUTS;78
5.5;5. THE DIRECTIONAL DISTANCE APPROACH;80
5.5.1;5.1 Efficiency measurement;80
5.5.2;5.2 Target setting;81
5.6;6. EFFICIENCY MEASUREMENT AND TARGET SETTING BY MEANS OF WEIGHTED ADDITIVE MODELS;82
5.6.1;6.1 Efficiency measurement;82
5.6.2;6.2 Target setting;83
5.7;7. ILLUSTRATIVE EXAMPLE;85
5.8;8. CONCLUSIONS;88
5.9;REFERENCES;88
6;Chapter 5 NON- DISCRETIONARY INPUTS;91
6.1;1. INTRODUCTION;91
6.2;2. PRODUCTION WITH NON-DISCRETIONARY INPUTS;93
6.3;3. THE BANKER AND MOREY MODEL ;96
6.3.1;3.1 Input-Oriented Model;96
6.3.2;3.2 Illustrative Example Using Simulated Data ;98
6.4;4. ALTERNATIVE DEA MODELS;101
6.4.1;4.1 Two-Stage Model Using DEA and Regression;101
6.4.2;4.2 Restricting Weights;102
6.4.3;4.3 Simulation Analysis;103
6.5;5. CONCLUSIONS;105
6.6;REFERENCES;106
7;Chapter 6 DEA WITH UNDESIRABLE FACTORS;108
7.1;1. INTRODUCTION;108
7.2;2. WEAK AND STRONG DISPOSABILITY OF UNDESIRABLE OUTPUTS;110
7.3;3. THE HYPERBOLIC OUTPUT EFFICIENCY MEASURE;111
7.4;4. A LINEAR TRANSFORMATION FOR UNDESIRABLE FACTORS;114
7.5;5. A DIRECTIONAL DISTANCE FUNCTION;115
7.6;6. NON-DISCRETIONARY INPUTS AND UNDESIRABLE OUTPUTS IN DEA;118
7.7;7. DISCUSSIONS AND CONCLUSION REMARKS;122
7.8;REFERENCES;125
8;Chapter 7 EUROPEAN NITRATE POLLUTION REGULATION AND FRENCH PIG FARMS’ PERFORMANCE;127
8.1;1. INTRODUCTION;128
8.2;2. MODELLING TECHNOLOGIES WITH GOOD AND BAD OUTPUTS;130
8.3;3. MODELLING TECHNOLOGIES WITH AN ENVIRONMENTAL STANDARD ON THE BY- OUTPUT;134
8.4;4. DATA AND EMPIRICAL MODEL;136
8.5;5. RESULTS;138
8.6;6. CONCLUSION;140
8.7;REFERENCES;141
8.8;ACKNOWLEDGEMENTS;142
9;Chapter 8 PCA- DEA;143
9.1;Reducing the curse of dimensionality;143
9.2;1. INTRODUCTION;143
9.3;2. DATA ENVELOPMENT ANALYSIS AND PRINCIPAL COMPONENT ANALYSIS;144
9.4;3. THE PCA-DEA CONSTRAINED MODEL FORMULATION;146
9.4.1;3.1 PCA-DEA model;146
9.4.2;3.2 PCA-DEA constrained model;149
9.5;4. APPLICATION OF THE PCA-DEA MODELS;151
9.6;5. SUMMARY AND CONCLUSIONS;154
9.7;REFERENCES;155
10;Chapter 9 MININGNONPARAMETRIC FRONTIERS;158
10.1;1. INTRODUCTION;158
10.2;2. THE DEA PARADIGM AND THE PRODUCTION POSSIBILITY SET;160
10.3;3. FRONTIER MINING;164
10.4;4. COMPUTATIONAL TESTS;170
10.5;5. CONCLUSIONS;172
10.6;REFERENCES;173
11;Chapter 10 DEA PRESENTED GRAPHICALLY USING MULTI- DIMENSIONAL SCALING;174
11.1;1. INTRODUCTION;174
11.2;2. CO-PLOT;176
11.3;3. CO-PLOT AND DEA;178
11.4;4. FINNISH FORESTRY BOARD ILLUSTRATION;180
11.5;5. CONCLUSIONS;187
11.6;REFERENCES;188
12;Chapter 11 DEA MODELS FOR SUPPLY CHAIN OR MULTI-STAGE STRUCTURE;191
12.1;1. INTRODUCTION;192
12.2;2. NOTIONS AND STANDARD DEA MODELS;193
12.3;3. ZHU (2003) APPROACH1;195
12.4;4. COOPERATIVE AND NON-COOPERATIVE APPROACHES;196
12.4.1;4.1 The Non-cooperative Model;196
12.4.2;4.2 The Cooperative Model;203
12.4.3;4.3 The Cooperative Model;207
12.5;5. CONCLUSIONS;208
12.6;REFERENCES;209
13;Chapter 12 NETWORK DEA;211
13.1;1. INTRODUCTION;212
13.2;2. STATIC NETWORK MODEL;213
13.3;3. DYNAMIC NETWORK MODEL;221
13.4;4. TECHNOLOGY ADOPTION;224
13.5;5. EPILOG;229
13.6;REFERENCES;230
14;Chapter 13 CONTEXT-DEPENDENT DATA ENVELOPMENT ANALYSIS AND ITS USE;243
14.1;1. INTRODUCTION;243
14.2;2. CONTEXT-DEPENDENT DATA ENVELOPMENT ANALYSIS ;245
14.2.1;2.1 Stratification DEA Model;245
14.2.2;2.2 Attractiveness and Progress;247
14.2.3;2.3 Output oriented context-dependent DEA model;248
14.2.4;2.4 Context-dependent DEA with Value Judgment;249
14.3;3. SLACK-BASED CONTEXT-DEPENDENT DEA;252
14.4;4. APPLICATION;255
14.5;5. CONCLUDING REMARKS;260
14.6;REFERENCES;260
15;Chapter 14 FLEXIBLE MEASURES-CLASSIFYING INPUTS AND OUTPUTS;262
15.1;1. INTRODUCTION;262
15.2;2. IDENTIFYING THE INPUT OUTPUT STATUS OF FLEXIBLE MEASURES;263
15.3;3. APPLICATION;268
15.4;4. CONCLUSIONS;270
15.5;REFERENCES;271
16;Chapter 15 INTEGER DEA MODELS;272
16.1;How DEA models can handle integer inputs and outputs;272
16.2;1. INTRODUCTION;272
16.3;2. INTEGER RADIAL DEA MODELS;274
16.4;3. ILLUSTRATION OF INTEGER RADIAL DEA MODEL;279
16.5;4. OTHER INTEGER DEA MODELS;282
16.6;5. CONCLUSIONS;288
16.7;REFERENCES;289
17;Chapter 16 DATA ENVELOPMENT ANALYSIS WITH MISSING DATA;291
17.1;A Reliable Solution Method;291
17.2;1. INTRODUCTION;291
17.3;2. THE FUZZY SET APPROACH;293
17.4;3. A CASE ANALYSIS;297
17.5;4. A COMPARISON;300
17.6;5. CONCLUSION;302
17.7;REFERENCES;302
18;Chapter 17 PREPARING YOUR DATA FOR DEA;305
18.1;1. SELECTION OF INPUTS AND OUTPUTS AND NUMBER OF DMUS;305
18.2;2. REDUCING DATA SETS FOR INPUT/OUTPUT FACTORS THAT ARE CORRELATED;308
18.3;3. IMBALANCE IN DATA MAGNITUDES;310
18.4;4. NEGATIVE NUMBERS AND ZERO VALUES4;312
18.5;5. MISSING DATA;317
18.6;REFERENCES;318
19;ABOUT THE AUTHORS;321
20;Index;331