Rezultati - "algorithms"
Priporočene teme znotraj vašega iskanja.
Priporočene teme znotraj vašega iskanja.
- Algorithms 132
- Computer algorithms 101
- Data processing 64
- Mathematics 62
- Mathematical models 58
- Computer science 54
- Economics 44
- Economics/Management Science 38
- Data mining 34
- Operations Research/Decision Theory 30
- Artificial intelligence 28
- Mathematical optimization 28
- Digital techniques 26
- Bioinformatics 24
- Computer Science 24
- Computer simulation 24
- Signal processing 24
- Data structures (Computer science) 23
- Computer software 22
- Image processing 22
- Data Mining and Knowledge Discovery 18
- Computational Biology/Bioinformatics 16
- Computational Science and Engineering 16
- Computer mathematics 16
- Genetic algorithms 16
- Machine learning 16
- Technological innovations 16
- Algorithm Analysis and Problem Complexity 14
- Computer programming 14
- Database management 14
-
721
Statistical monitoring of complex multivariate processes with applications in industrial process control /
Izdano 2012Kazalo: “...Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 6 Further Modeling Issues 6.1 Accuracy of Estimating PCA Models 6.1.1 Revisiting the Eigendecomposition of Sz0z0 6.1.2 Two Illustrative Examples 6.1.3 Maximum Likelihood PCA for Known Sgg 6.1.4 Maximum Likelihood PCA for Unknown Sgg 6.1.5 A Simulation Example 6.1.6 A Stopping Rule for Maximum Likelihood PCA Models 6.1.7 Properties of Model and Residual Subspace Estimates 6.1.8 Application to a Chemical Reaction Process - Revisited 6.2 Accuracy of Estimating PLS Models 6.2.1 Bias and Variance of Parameter Estimation 6.2.2 Comparing Accuracy of PLS and OLS Regression Models 6.2.3 Impact of Error-in-Variables Structure upon PLS Models 6.2.4 Error-in-Variable Estimate for Known See 6.2.5 Error-in-Variable Estimate for Unknown See 6.2.6 Application to a Distillation Process - Revisited 6.3 Robust Model Estimation 6.3.1 Robust Parameter Estimation 6.3.2 Trimming Approaches 6.4 Small Sample Sets 6.5 Tutorial Session 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow Principal Component Analysis 7.3.1 Adapting the Data Correlation Matrix 7.3.2 Adapting the Eigendecomposition 7.3.3 Computational Analysis of the Adaptation Procedure 7.3.4 Adaptation of Control Limits 7.3.5 Process Monitoring using an Application Delay 7.3.6 MinimumWindow Length 7.4 A Simulation Example 7.4.1 Data Generation 7.4.2 Application of PCA 7.4.3 Utilizing MWPCA based on an Application Delay 7.5 Application to a Fluid Catalytic Cracking Unit 7.5.1 Process Description 7.5.2 Data Generation 7.5.3 Pre-analysis of Simulated Data 7.5.4 Application of PCA 7.5.5 Application of MWPCA 7.6 Application to a Furnace Process 7.6.1 Process Description 7.6.2 Description of Sensor Bias 7.6.3 Application of PCA 7.6.4 Utilizing MWPCA based on an Application Delay 7.7 Adaptive Partial Least Squares 7.7.1 Recursive Adaptation of Sx0x0 and Sx0y0 7.7.2 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 8 Monitoring Changes in Covariance Structure 8.1 Problem Analysis 8.1.1 First Intuitive Example 8.1.2 Generic Statistical Analysis 8.1.3 Second Intuitive Example 8.2 Preliminary Discussion of Related Techniques 8.3 Definition of Primary and Improved Residuals 8.3.1 Primary Residuals for Eigenvectors 8.3.2 Primary Residuals for Eigenvalues 8.3.3 Comparing both Types of Primary Residuals 8.3.4 Statistical Properties of Primary Residuals 8.3.5 Improved Residuals for Eigenvalues 8.4 Revisiting the Simulation Examples in Section 8.1 8.4.1 First Simulation Example 8.4.2 Second Simulation Example 8.5 Fault Isolation and Identification 8.5.1 Diagnosis of Step-Type Fault Conditions 8.5.2 Diagnosis of General Deterministic Fault Conditions 8.5.3 A Simulation Example 8.6 Application Study to a Gearbox System 8.6.1 Process Description 8.6.2 Fault Description 8.6.3 Identification of a Monitoring Model 8.6.4 Detecting a Fault Condition 8.7 Analysis of Primary and Improved Residuals 8.7.1 Central Limit Theorem 8.7.2 Further Statistical Properties of Primary Residuals 8.7.3 Sensitivity of Statistics based on Improved Residuals 8.8 Tutorial Session IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index....”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
722
Statistical monitoring of complex multivariate processes with applications in industrial process control /
Izdano 2012Kazalo: “...Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 6 Further Modeling Issues 6.1 Accuracy of Estimating PCA Models 6.1.1 Revisiting the Eigendecomposition of Sz0z0 6.1.2 Two Illustrative Examples 6.1.3 Maximum Likelihood PCA for Known Sgg 6.1.4 Maximum Likelihood PCA for Unknown Sgg 6.1.5 A Simulation Example 6.1.6 A Stopping Rule for Maximum Likelihood PCA Models 6.1.7 Properties of Model and Residual Subspace Estimates 6.1.8 Application to a Chemical Reaction Process - Revisited 6.2 Accuracy of Estimating PLS Models 6.2.1 Bias and Variance of Parameter Estimation 6.2.2 Comparing Accuracy of PLS and OLS Regression Models 6.2.3 Impact of Error-in-Variables Structure upon PLS Models 6.2.4 Error-in-Variable Estimate for Known See 6.2.5 Error-in-Variable Estimate for Unknown See 6.2.6 Application to a Distillation Process - Revisited 6.3 Robust Model Estimation 6.3.1 Robust Parameter Estimation 6.3.2 Trimming Approaches 6.4 Small Sample Sets 6.5 Tutorial Session 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow Principal Component Analysis 7.3.1 Adapting the Data Correlation Matrix 7.3.2 Adapting the Eigendecomposition 7.3.3 Computational Analysis of the Adaptation Procedure 7.3.4 Adaptation of Control Limits 7.3.5 Process Monitoring using an Application Delay 7.3.6 MinimumWindow Length 7.4 A Simulation Example 7.4.1 Data Generation 7.4.2 Application of PCA 7.4.3 Utilizing MWPCA based on an Application Delay 7.5 Application to a Fluid Catalytic Cracking Unit 7.5.1 Process Description 7.5.2 Data Generation 7.5.3 Pre-analysis of Simulated Data 7.5.4 Application of PCA 7.5.5 Application of MWPCA 7.6 Application to a Furnace Process 7.6.1 Process Description 7.6.2 Description of Sensor Bias 7.6.3 Application of PCA 7.6.4 Utilizing MWPCA based on an Application Delay 7.7 Adaptive Partial Least Squares 7.7.1 Recursive Adaptation of Sx0x0 and Sx0y0 7.7.2 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 8 Monitoring Changes in Covariance Structure 8.1 Problem Analysis 8.1.1 First Intuitive Example 8.1.2 Generic Statistical Analysis 8.1.3 Second Intuitive Example 8.2 Preliminary Discussion of Related Techniques 8.3 Definition of Primary and Improved Residuals 8.3.1 Primary Residuals for Eigenvectors 8.3.2 Primary Residuals for Eigenvalues 8.3.3 Comparing both Types of Primary Residuals 8.3.4 Statistical Properties of Primary Residuals 8.3.5 Improved Residuals for Eigenvalues 8.4 Revisiting the Simulation Examples in Section 8.1 8.4.1 First Simulation Example 8.4.2 Second Simulation Example 8.5 Fault Isolation and Identification 8.5.1 Diagnosis of Step-Type Fault Conditions 8.5.2 Diagnosis of General Deterministic Fault Conditions 8.5.3 A Simulation Example 8.6 Application Study to a Gearbox System 8.6.1 Process Description 8.6.2 Fault Description 8.6.3 Identification of a Monitoring Model 8.6.4 Detecting a Fault Condition 8.7 Analysis of Primary and Improved Residuals 8.7.1 Central Limit Theorem 8.7.2 Further Statistical Properties of Primary Residuals 8.7.3 Sensitivity of Statistics based on Improved Residuals 8.8 Tutorial Session IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index....”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
723
Handbook of exchange rates
Izdano 2012Kazalo: “...High frequency finance: Using scaling laws to build trading models 21. Algorithmic Execution in Foreign Exchange 22. Foreign Exchange Strategy Based Products 23. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
724
The dynamic brain : an exploration of neuronal variability and its functional significance /
Izdano 2011Kazalo: “...A mixed-filter algorithm for dynamically tracking learning from multiple behavioral and neurophysiological measures / Todd P. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
725
Handbook of exchange rates
Izdano 2012Kazalo: “...High frequency finance: Using scaling laws to build trading models 21. Algorithmic Execution in Foreign Exchange 22. Foreign Exchange Strategy Based Products 23. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
726
The dynamic brain : an exploration of neuronal variability and its functional significance /
Izdano 2011Kazalo: “...A mixed-filter algorithm for dynamically tracking learning from multiple behavioral and neurophysiological measures / Todd P. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
727
Computation and the Humanities Towards an Oral History of Digital Humanities /
Izdano 2016Kazalo: “...-- ‘Individuation is There in all the Different Strata:’ an Oral History Conversation between John Burrows, Hugh Craig and Willard McCarty -- ‘It was a Time When the University was Still Taking Account of the Meaning of universitas scientiarum’: an Oral History Conversation between Wilhelm Ott and Julianne Nyhan -- ‘hic Rhodus, hic salta’: An Oral History Interview Between Tito Orlandi and Julianne Nyhan -- ‘They Took a Chance’: An Oral History Conversation between Susan Hockey and Julianne Nyhan -- ‘And Here We go Back Again to the Influence of Algorithmic Thinking’: An Oral History conversation between Judy Malloy and Julianne Nyhan -- ‘I Would Think of Myself as Sitting Inside the Computer, Moving Things Around in Order to Accomplish the Goal of my Programming’: An Oral History Conversation Between Mary Dee Harris and Julianne Nyhan -- ‘I Was Absolutely Convinced That There Had to be a Better Way’: An Oral History Conversation Between John Nitti and Julianne Nyhan -- ‘It’s a Little Mind-Boggling Actually’: An Oral History Conversation between Helen Agüera and Julianne Nyhan -- ‘I Heard About the Arrival of the Computer’: An Oral History Conversation Between Hans Rutimann and Julianne Nyhan -- ‘Langezeit habe ich der Universitaet nachgetrauert’: An Oral History Conversation between Michael Sperberg-McQueen and Julianne Nyhan....”
Polni tekst
Elektronski eKnjiga -
728
Computation and the Humanities Towards an Oral History of Digital Humanities /
Izdano 2016Kazalo: “...-- ‘Individuation is There in all the Different Strata:’ an Oral History Conversation between John Burrows, Hugh Craig and Willard McCarty -- ‘It was a Time When the University was Still Taking Account of the Meaning of universitas scientiarum’: an Oral History Conversation between Wilhelm Ott and Julianne Nyhan -- ‘hic Rhodus, hic salta’: An Oral History Interview Between Tito Orlandi and Julianne Nyhan -- ‘They Took a Chance’: An Oral History Conversation between Susan Hockey and Julianne Nyhan -- ‘And Here We go Back Again to the Influence of Algorithmic Thinking’: An Oral History conversation between Judy Malloy and Julianne Nyhan -- ‘I Would Think of Myself as Sitting Inside the Computer, Moving Things Around in Order to Accomplish the Goal of my Programming’: An Oral History Conversation Between Mary Dee Harris and Julianne Nyhan -- ‘I Was Absolutely Convinced That There Had to be a Better Way’: An Oral History Conversation Between John Nitti and Julianne Nyhan -- ‘It’s a Little Mind-Boggling Actually’: An Oral History Conversation between Helen Agüera and Julianne Nyhan -- ‘I Heard About the Arrival of the Computer’: An Oral History Conversation Between Hans Rutimann and Julianne Nyhan -- ‘Langezeit habe ich der Universitaet nachgetrauert’: An Oral History Conversation between Michael Sperberg-McQueen and Julianne Nyhan....”
Polni tekst
Elektronski eKnjiga -
729
Understanding ultrasonic level measurement
Izdano 2013Kazalo: “.... -- Ultrasonic instrumentation -- The transducer -- Transducer environments -- Transducer accuracy -- Transducer resolution and accuracy -- Impedance matching -- Axis of transmission -- Beam width -- Beam spreading -- Ringdown -- The controllers -- Digital filtering -- Averaging echoes -- Echo extraction algorithms -- Summary -- Notes --...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
730
Understanding ultrasonic level measurement
Izdano 2013Kazalo: “.... -- Ultrasonic instrumentation -- The transducer -- Transducer environments -- Transducer accuracy -- Transducer resolution and accuracy -- Impedance matching -- Axis of transmission -- Beam width -- Beam spreading -- Ringdown -- The controllers -- Digital filtering -- Averaging echoes -- Echo extraction algorithms -- Summary -- Notes --...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
731
Heat transfer virtual lab for students and engineers : theory and guide for setting up /
Izdano 2014Kazalo: “...Design of LabVIEW VI program -- 4.1 Software: algorithm of the program -- 4.2 Introduction of LabVIEW controls used in the project -- 4.3 Design of front panel -- 4.4 Design of block diagram -- 4.5 How were the PID parameters' values derived for temperature control? ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
732
Heat transfer virtual lab for students and engineers : theory and guide for setting up /
Izdano 2014Kazalo: “...Design of LabVIEW VI program -- 4.1 Software: algorithm of the program -- 4.2 Introduction of LabVIEW controls used in the project -- 4.3 Design of front panel -- 4.4 Design of block diagram -- 4.5 How were the PID parameters' values derived for temperature control? ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
733
Statistical disclosure control
Izdano 2012Kazalo: “...Machine generated contents note: Preface vii Acknowledgements ix 1 Introduction 1 1.1 Concepts and Definitions 2 1.1.1 Disclosure 2 1.1.2 Statistical disclosure control 2 1.1.3 Tabular data 3 1.1.4 Microdata 3 1.1.5 Risk and utility 4 1.2 An approach to Statistical Disclosure Control 6 1.3 The chapters of the handbook 8 2 Ethics, Principles, Guidelines and Regulations, a general background 9 2.1 Introduction 9 2.2 Ethical codes and the new ISI code 9 2.2.1 ISI Declaration on Professional Ethics 10 2.2.2 New ISI Declaration on Professional Ethics 10 2.2.3 European Statistics Code of Practice 14 2.3 UNECE Principles and guidelines 14 2.4 Laws 17 2.4.1 Committee on Statistical Confidentiality 18 2.4.2 European Statistical System Committee 18 3 Microdata 21 3.1 Introduction 21 3.2 Microdata Concepts 22 3.2.1 Stage 1: Assess need for confidentiality protection 22 3.2.2 Stage 2: Key characteristics and uses of microdata 24 3.2.3 Stage 3: Disclosure risk 27 3.2.4 Stage 4: Protection methods 29 3.2.5 Stage 5: Implementation 30 3.3 Definitions of disclosure 32 3.3.1 Definitions of disclosure scenarios 33 3.4 Definitions of Disclosure Risk 34 3.4.1 Disclosure risk for categorical quasi-identifiers 35 3.4.2 Disclosure risk for continuous quasi-identifiers 37 3.5 Estimating Re-identification Risk 39 3.5.1 Individual risk based on the sample: threshold rule 39 3.5.2 Estimating individual risk using sampling weights 39 3.5.3 Estimating individual risk by Poisson model 42 3.5.4 Further models that borrow information from other sources 43 3.5.5 Estimating per record risk via heuristics 44 3.5.6 Assessing risk via record linkage 45 3.6 Non-Perturbative Microdata Masking 45 3.6.1 Sampling 46 3.6.2 Global recoding 46 3.6.3 Top and bottom coding 47 3.6.4 Local suppression 47 3.7 Perturbative Microdata Masking 48 3.7.1 Additive noise masking 48 3.7.2 Multiplicative noise masking 52 3.7.3 Microaggregation 54 3.7.4 Data swapping and rank swapping 66 3.7.5 Data shuffling 66 3.7.6 Rounding 67 3.7.7 Resampling 67 3.7.8 PRAM 67 3.7.9 MASSC 71 3.8 Synthetic and Hybrid Data 71 3.8.1 Fully synthetic data 72 3.8.2 Partially synthetic data 77 3.8.3 Hybrid data 79 3.8.4 Pros and cons of synthetic and hybrid data 88 3.9 Information Loss in Microdata 91 3.9.1 Information loss measures for continuous data 92 3.9.2 Information loss measures for categorical data 99 3.10 Release of multiple files from the same microdata set 101 3.11 Software 102 3.11.1 _-ARGUS 102 3.11.2 sdcMicro 103 3.11.3 IVEware 106 3.12 Case Studies 106 3.12.1 Microdata files at Statistics Netherlands 106 3.12.2 The European Labour Force Survey Microdata for Research Purposes 108 3.12.3 The European Structure of Earnings Survey Microdata for Research Purposes 111 3.12.4 NHIS Linked Mortality Data Public Use File, USA 117 3.12.5 Other real case instances 119 4 Magnitude tabular data 121 4.1 Introduction 121 4.1.1 Magnitude Tabular Data: Basic Terminology 121 4.1.2 Complex tabular data structures: hierarchical and linked tables 122 4.1.3 Risk Concepts 124 4.1.4 Protection Concepts 127 4.1.5 Information Loss Concepts 127 4.1.6 Implementation: Software, Guidelines and Case Study 127 4.2 Disclosure Risk Assessment I: Primary Sensitive Cells 128 4.2.1 Intruder Scenarios 128 4.2.2 Sensitivity rules 129 4.3 Disclosure Risk Assessment II: Secondary risk assessment 140 4.3.1 Feasibility Interval 141 4.3.2 Protection Level 142 4.3.3 Singleton and multi cell disclosure 143 4.3.4 Risk models for hierarchical and linked tables 144 4.4 Non-Perturbative Protection Methods 145 4.4.1 Global Recoding 145 4.4.2 The Concept of Cell Suppression 145 4.4.3 Algorithms for Secondary Cell Suppression 146 4.4.4 Secondary Cell Suppression in Hierarchical and Linked Tables 149 4.5 Perturbative Protection Methods 151 4.5.1 A pre-tabular method: Multiplicative Noise 152 4.5.2 A Post-tabular Method: Controlled Tabular Adjustment 153 4.6 Information Loss Measures for Tabular Data 153 4.6.1 Cell Costs for Cell Suppression 153 4.6.2 Cell Costs for CTA 154 4.6.3 Information Loss Measures to Evaluate the Outcome of Table Protection 155 4.7 Software for Tabular Data Protection 155 4.7.1 Empirical comparison of cell suppression algorithms 156 4.8 Guidelines: Setting up an efficient table model systematically 160 4.8.1 Defining Spanning Variables 161 4.8.2 Response Variables and Mapping Rules 162 4.9 Case Studies 164 4.9.1 Response Variables and Mapping Rules of the Case Study 164 4.9.2 Spanning Variables of the Case Study 165 4.9.3 Analysing the Tables of the Case Study 165 4.9.4 Software Issues of the Case Study 167 5 Frequency tables 169 5.1 Introduction 169 5.2 Disclosure risks 169 5.3 Methods 176 5.4 Post-tabular methods 178 5.4.1 Cell Suppression 178 5.4.2 ABS Cell Perturbation 179 5.4.3 Rounding 179 5.5 Information loss 184 5.6 Software 186 5.6.1 Introduction 186 5.7 Case Studies 188 5.7.1 UK Census 188 5.7.2 Australian and New Zealand Censuses 190 6 Data Access Issues 193 6.1 Introduction 193 6.2 Research Data Centres 193 6.3 Remote Execution 194 6.4 Remote Access 195 6.5 Licensing 196 6.6 Guidelines on output checking 196 6.6.1 Introduction 196 6.6.2 General approach 197 6.6.3 Rules for output checking 199 6.6.4 Organizational/procedural aspects of output checking 208 6.6.5 Researcher training 215 6.7 Additional issues concerning data access 218 6.7.1 Examples of disclaimers 218 6.7.2 Output description 218 6.8 Case Studies 219 6.8.1 The U.S. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
734
Statistical disclosure control
Izdano 2012Kazalo: “...Machine generated contents note: Preface vii Acknowledgements ix 1 Introduction 1 1.1 Concepts and Definitions 2 1.1.1 Disclosure 2 1.1.2 Statistical disclosure control 2 1.1.3 Tabular data 3 1.1.4 Microdata 3 1.1.5 Risk and utility 4 1.2 An approach to Statistical Disclosure Control 6 1.3 The chapters of the handbook 8 2 Ethics, Principles, Guidelines and Regulations, a general background 9 2.1 Introduction 9 2.2 Ethical codes and the new ISI code 9 2.2.1 ISI Declaration on Professional Ethics 10 2.2.2 New ISI Declaration on Professional Ethics 10 2.2.3 European Statistics Code of Practice 14 2.3 UNECE Principles and guidelines 14 2.4 Laws 17 2.4.1 Committee on Statistical Confidentiality 18 2.4.2 European Statistical System Committee 18 3 Microdata 21 3.1 Introduction 21 3.2 Microdata Concepts 22 3.2.1 Stage 1: Assess need for confidentiality protection 22 3.2.2 Stage 2: Key characteristics and uses of microdata 24 3.2.3 Stage 3: Disclosure risk 27 3.2.4 Stage 4: Protection methods 29 3.2.5 Stage 5: Implementation 30 3.3 Definitions of disclosure 32 3.3.1 Definitions of disclosure scenarios 33 3.4 Definitions of Disclosure Risk 34 3.4.1 Disclosure risk for categorical quasi-identifiers 35 3.4.2 Disclosure risk for continuous quasi-identifiers 37 3.5 Estimating Re-identification Risk 39 3.5.1 Individual risk based on the sample: threshold rule 39 3.5.2 Estimating individual risk using sampling weights 39 3.5.3 Estimating individual risk by Poisson model 42 3.5.4 Further models that borrow information from other sources 43 3.5.5 Estimating per record risk via heuristics 44 3.5.6 Assessing risk via record linkage 45 3.6 Non-Perturbative Microdata Masking 45 3.6.1 Sampling 46 3.6.2 Global recoding 46 3.6.3 Top and bottom coding 47 3.6.4 Local suppression 47 3.7 Perturbative Microdata Masking 48 3.7.1 Additive noise masking 48 3.7.2 Multiplicative noise masking 52 3.7.3 Microaggregation 54 3.7.4 Data swapping and rank swapping 66 3.7.5 Data shuffling 66 3.7.6 Rounding 67 3.7.7 Resampling 67 3.7.8 PRAM 67 3.7.9 MASSC 71 3.8 Synthetic and Hybrid Data 71 3.8.1 Fully synthetic data 72 3.8.2 Partially synthetic data 77 3.8.3 Hybrid data 79 3.8.4 Pros and cons of synthetic and hybrid data 88 3.9 Information Loss in Microdata 91 3.9.1 Information loss measures for continuous data 92 3.9.2 Information loss measures for categorical data 99 3.10 Release of multiple files from the same microdata set 101 3.11 Software 102 3.11.1 _-ARGUS 102 3.11.2 sdcMicro 103 3.11.3 IVEware 106 3.12 Case Studies 106 3.12.1 Microdata files at Statistics Netherlands 106 3.12.2 The European Labour Force Survey Microdata for Research Purposes 108 3.12.3 The European Structure of Earnings Survey Microdata for Research Purposes 111 3.12.4 NHIS Linked Mortality Data Public Use File, USA 117 3.12.5 Other real case instances 119 4 Magnitude tabular data 121 4.1 Introduction 121 4.1.1 Magnitude Tabular Data: Basic Terminology 121 4.1.2 Complex tabular data structures: hierarchical and linked tables 122 4.1.3 Risk Concepts 124 4.1.4 Protection Concepts 127 4.1.5 Information Loss Concepts 127 4.1.6 Implementation: Software, Guidelines and Case Study 127 4.2 Disclosure Risk Assessment I: Primary Sensitive Cells 128 4.2.1 Intruder Scenarios 128 4.2.2 Sensitivity rules 129 4.3 Disclosure Risk Assessment II: Secondary risk assessment 140 4.3.1 Feasibility Interval 141 4.3.2 Protection Level 142 4.3.3 Singleton and multi cell disclosure 143 4.3.4 Risk models for hierarchical and linked tables 144 4.4 Non-Perturbative Protection Methods 145 4.4.1 Global Recoding 145 4.4.2 The Concept of Cell Suppression 145 4.4.3 Algorithms for Secondary Cell Suppression 146 4.4.4 Secondary Cell Suppression in Hierarchical and Linked Tables 149 4.5 Perturbative Protection Methods 151 4.5.1 A pre-tabular method: Multiplicative Noise 152 4.5.2 A Post-tabular Method: Controlled Tabular Adjustment 153 4.6 Information Loss Measures for Tabular Data 153 4.6.1 Cell Costs for Cell Suppression 153 4.6.2 Cell Costs for CTA 154 4.6.3 Information Loss Measures to Evaluate the Outcome of Table Protection 155 4.7 Software for Tabular Data Protection 155 4.7.1 Empirical comparison of cell suppression algorithms 156 4.8 Guidelines: Setting up an efficient table model systematically 160 4.8.1 Defining Spanning Variables 161 4.8.2 Response Variables and Mapping Rules 162 4.9 Case Studies 164 4.9.1 Response Variables and Mapping Rules of the Case Study 164 4.9.2 Spanning Variables of the Case Study 165 4.9.3 Analysing the Tables of the Case Study 165 4.9.4 Software Issues of the Case Study 167 5 Frequency tables 169 5.1 Introduction 169 5.2 Disclosure risks 169 5.3 Methods 176 5.4 Post-tabular methods 178 5.4.1 Cell Suppression 178 5.4.2 ABS Cell Perturbation 179 5.4.3 Rounding 179 5.5 Information loss 184 5.6 Software 186 5.6.1 Introduction 186 5.7 Case Studies 188 5.7.1 UK Census 188 5.7.2 Australian and New Zealand Censuses 190 6 Data Access Issues 193 6.1 Introduction 193 6.2 Research Data Centres 193 6.3 Remote Execution 194 6.4 Remote Access 195 6.5 Licensing 196 6.6 Guidelines on output checking 196 6.6.1 Introduction 196 6.6.2 General approach 197 6.6.3 Rules for output checking 199 6.6.4 Organizational/procedural aspects of output checking 208 6.6.5 Researcher training 215 6.7 Additional issues concerning data access 218 6.7.1 Examples of disclaimers 218 6.7.2 Output description 218 6.8 Case Studies 219 6.8.1 The U.S. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
735
-
736
-
737
Quantitative evaluation of HIV prevention programs
Izdano 2002Kazalo: “...Development and Validation of a Serologic Testing Algorithm for Recent HIV Seroconversion GlenA. Satten, -- Robert S. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
738
Quantitative evaluation of HIV prevention programs
Izdano 2002Kazalo: “...Development and Validation of a Serologic Testing Algorithm for Recent HIV Seroconversion GlenA. Satten, -- Robert S. ...”
An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
739
Sustainable production automation /
Izdano 2017An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga -
740
Sustainable production automation /
Izdano 2017An electronic book accessible through the World Wide Web; click to view
Elektronski eKnjiga