Suchergebnisse

Preface -- Overview of the Book -- Acknowledgements -- Contents -- Acronyms -- 1 Software -- 1.1 Prerequisites -- 1.1.1 Installation and Updates -- 1.1.2 Install sdcMicro and Its Browser-Based Point-and-Click App -- 1.1.3 Updating the SDC Tools -- 1.1.4 Help -- 1.1.5 The R Workspace and the Working Directory -- 1.1.6 Data Types -- 1.1.7 Generic Functions, Methods and Classes -- 1.2 Brief Overview on SDC Software Tools -- 1.3 Differences Between SDC Tools -- 1.4 Working with sdcMicro -- 1.4.1 General Information About sdcMicro -- 1.4.2 S4 Class Structure of the sdcMicro Package -- 1.4.3 Utility Functions -- 1.4.4 Reporting Facilities -- 1.5 The Point-and-Click App sdcApp -- 1.6 The simPop package -- References -- 2 Basic Concepts -- 2.1 Types of Variables -- 2.1.1 Non-confidential Variables -- 2.1.2 Identifying Variables -- 2.1.3 Sensitive Variables -- 2.1.4 Linked Variables -- 2.1.5 Sampling Weights -- 2.1.6 Hierarchies, Clusters and Strata -- 2.1.7 Categorical Versus Continuous Variables -- 2.2 Types of Disclosure -- 2.2.1 Identity Disclosure -- 2.2.2 Attribute Disclosure -- 2.2.3 Inferential Disclosure -- 2.3 Disclosure Risk Versus Information Loss and Data Utility -- 2.4 Release Types -- 2.4.1 Public Use Files (PUF) -- 2.4.2 Scientific Use Files (SUF) -- 2.4.3 Controlled Research Data Center -- 2.4.4 Remote Execution -- 2.4.5 Remote Access -- References -- 3 Disclosure Risk -- 3.1 Introduction -- 3.2 Frequency Counts -- 3.2.1 The Number of Cells of Equal Size -- 3.2.2 Frequency Counts with Missing Values -- 3.2.3 Sample Frequencies in sdcMicro -- 3.3 Principles of k-anonymity and l-diversity -- 3.3.1 Simplified Estimation of Population Frequency Counts -- 3.4 Special Uniques Detection Algorithm (SUDA) -- 3.4.1 Minimal Sample Uniqueness -- 3.4.2 SUDA Scores -- 3.4.3 SUDA DIS Scores -- 3.4.4 SUDA in sdcMicro -- 3.5 The Individual Risk Approach

The R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the headcount ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of datadriven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria.