Analysis of integrated data

목차 (Table of Contents)

• CONTENTS
• Preface = xiii
• Contributors = xv
• 1 Introduction / Raymond L. Chambers = 1
• 1.1 Why this book? = 1
• 1.2 The structure of this book = 3
• 1.3 Summary = 11
• References = 11
• 2 On secondary analysis of datasets that cannot be linked without errors / Li-Chun Zhang = 13
• 2.1 Introduction = 13
• 2.1.1 Related work = 14
• 2.1.2 Outline of investigation = 15
• 2.2 The linkage data structure = 16
• 2.2.1 Definitions = 17
• 2.2.2 Agreement partition of match space = 18
• 2.3 On maximum likelihood estimation = 20
• 2.4 On analysis under the comparison data model = 22
• 2.4.1 Linear regression under the linkage model = 22
• 2.4.2 Linear regression under the comparison data model = 24
• 2.4.3 Comparison data modelling (Ⅰ) = 25
• 2.4.4 Comparison data modelling (Ⅱ) = 27
• 2.5 On link subset analysis = 30
• 2.5.1 Non-informative balanced selection = 30
• 2.5.2 Illustration for the C-PR data = 33
• 2.6 Concluding remarks = 34
• Bibliography = 35
• 3 Capture-recapture methods in the presence of linkage errors / Loredana Di Consiglio ; Tiziana Tuoto ; Li-Chun Zhang = 39
• 3.1 Introduction = 39
• 3.2 The capture-recapture model : short formalization and notation = 40
• 3.3 The linkage models and the linkage errors = 42
• 3.3.1 The Fellegi and Sunter linkage model = 42
• 3.3.2 Definition and estimation of linkage errors = 44
• 3.3.3 Bayesian approaches to record linkage = 45
• 3.4 The DSE in the presence of linkage errors = 47
• 3.4.1 The Ding and Fienberg estimator = 47
• 3.4.2 The modified Ding and Fienberg estimator = 48
• 3.4.3 Some remarks = 49
• 3.4.4 Examples = 52
• 3.5 Linkage-error adjustments in the case of multiple lists = 57
• 3.5.1 Log-linear model-based estimators = 57
• 3.5.2 An alternative modelling approach = 60
• 3.5.3 A Bayesian proposal = 61
• 3.5.4 Examples = 62
• 3.6 Concluding remarks = 65
• Bibliography = 66
• 4 An overview on uncertainty and estimation in statistical matching / Pier Luigi Conti ; Daniela Marella ; Mauro Scanu = 73
• 4.1 Introduction = 73
• 4.2 Statistical matching problem : notations and technicalities = 75
• 4.3 The joint distribution of variables not jointly observed : estimation and uncertainty = 77
• 4.3.1 Matching error = 81
• 4.3.2 Bounding the matching error via measures of uncertainty = 83
• 4.4 Statistical matching for complex sample surveys = 87
• 4.4.1 Technical assumptions on the sample designs = 88
• 4.4.2 A proposal for choosing a matching distribution = 90
• 4.4.3 Reliability of the matching distribution = 91
• 4.4.4 Evaluation of the matching reliability as a hypothesis problem = 93
• 4.5 Conclusions and pending issues : relationship between the statistical matching problem and ecological inference = 94
• Bibliography = 96
• 5 Auxiliary variable selection in a statistical matching problem / Marcello D'Orazio ; Marco Di Zio ; Mauro Scanu = 101
• 5.1 Introduction = 101
• 5.2 Choice of the matching variables = 103
• 5.2.1 Traditional methods based on association = 104
• 5.2.2 Choosing the matching variables by uncertainty reduction = 105
• 5.2.3 An illustrative example = 106
• 5.2.4 The penalised uncertainty measure = 109
• 5.3 Simulations with European Social Survey data = 111
• 5.4 Conclusions = 117
• Bibliography = 117
• 6 Minimal inference from incomplete 2 × 2-tables / Li-Chun Zhang ; Raymond L. Chambers = 121
• 6.1 Introduction = 121
• 6.2 Corroboration = 125
• 6.3 Maximum corroboration set = 127
• 6.4 High assurance estimation of Θ = 130
• 6.5 A corroboration test = 131
• 6.6 Application : missing OCBGT data = 132
• Bibliography = 133
• 7 Dual- and multiple-system estimation with fully and partially observed covariates / Peter G. M. van der Heijden ; Paul A. Smith ; Joe Whittaker ; Maarten Cruyff ; Bart F. M. Bakker = 137
• 7.1 Introduction = 138
• 7.2 Theory concerning invariant population-size estimates = 140
• 7.2.1 Terminology and properties = 140
• 7.2.2 Example = 142
• 7.2.3 Graphical representation of log-linear models = 144
• 7.2.4 Three registers = 145
• 7.3 Applications of invariant population-size estimation = 146
• 7.3.1 Modelling strategies with active and passive covariates = 146
• 7.3.2 Working with invariant population-size estimates = 147
• 7.4 Dealing with partially observed covariates = 148
• 7.4.1 Framework for population-size estimation with partially observed covariates = 148
• 7.4.2 Example = 150
• 7.4.3 Interaction graphs for models with incomplete covariates = 152
• 7.4.4 Results of model fitting = 152
• 7.5 Precision and sensitivity = 154
• 7.5.1 Precision = 154
• 7.5.2 Sensitivity = 156
• 7.5.3 Comparison of the EM algorithm with the classical model = 157
• 7.6 An application when the same variable is measured differently in both registers = 157
• 7.6.1 Example : Injuries in road accidents in the Netherlands = 158
• 7.6.2 More detailed breakdown of transport mode in accidents = 160
• 7.7 Discussion = 161
• 7.7.1 Alternative approaches = 161
• 7.7.2 Quality issues = 164
• Bibliography = 165
• 8 Estimating population size in multiple record systems with uncertainty of state identification / Davide Di Cecco = 169
• 8.1 Introduction = 169
• 8.2 A latent class model for capture-recapture = 172
• 8.2.1 Decomposable models = 174
• 8.2.2 Identifiability = 176
• 8.2.3 EM algorithm = 176
• 8.2.4 Fixing parameters = 178
• 8.2.5 A mixture of different components = 178
• 8.2.6 Model selection = 179
• 8.3 Observed heterogeneity of capture probabilities = 181
• 8.3.1 Use of covariates = 181
• 8.3.2 Incomplete lists = 182
• 8.4 Evaluating the interpretation of the latent classes = 186
• 8.5 A Bayesian approach = 187
• 8.5.1 MCMC algorithm = 189
• 8.5.2 Simulations results = 191
• Bibliography = 192
• 9 Log-linear models of erroneous list data / Li-Chun Zhang = 197
• 9.1 Introduction = 197
• 9.2 Log-linear models of incomplete contingency tables = 199
• 9.3 Modelling marginally classified list errors = 200
• 9.3.1 The models = 200
• 9.3.2 Maximum likelihood estimation = 203
• 9.3.3 Estimation based on list-survey data = 204
• 9.4 Model selection with zero degree of freedom = 206
• 9.4.1 Latent likelihood ratio criterion = 206
• 9.4.2 Illustration = 209
• 9.5 Homelessness data in the Netherlands = 212
• 9.5.1 Data and previous study = 212
• 9.5.2 Analysis allowing for erroneous enumeration = 213
• Bibliography = 217
• 10 Sampling design and analysis using geo-referenced data / Danila Filipponi ; Federica Piersimoni ; Roberto Benedetti ; Maria Michela Dickson ; Giuseppe Espa ; Diego Giuliani = 219
• 10.1 Introduction = 219
• 10.2 Geo-referenced data and potential locational errors = 221
• 10.3 A brief review of spatially balanced sampling methods = 222
• 10.3.1 Local pivotal methods = 223
• 10.3.2 Spatially correlated Poisson sampling = 224
• 10.3.3 Balanced sampling through the cube method = 225
• 10.3.4 Local cube method = 225
• 10.4 Spatial sampling for estimation of under-coverage rate = 226
• 10.5 Business surveys in the presence of locational errors = 232
• 10.6 Conclusions = 239
• Bibliography = 240
• Index = 247