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Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis A Problem-Solving Handbook

:	J. Zhu, W. D. Cook
:	Joe Zhu, Wade D. Cook
:	Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis A Problem-Solving Handbook
:	Springer-Verlag
:	9780387716077
:	1
:	CHF 85.40
:

:	Allgemeines, Lexika
:	English

:	334
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

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.

	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	19
	21	19
	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
	REFEREN