New Developments in Statistical Disclosure Control and Imputation | Robust Statistics Applied to Official Statistics

Matthias Templ

ISBN 10: 3838108280 / ISBN 13: 9783838108285

Published by Südwestdeutscher Verlag für Hochschulschriften, 2015

About this Item

New Developments in Statistical Disclosure Control and Imputation | Robust Statistics Applied to Official Statistics | Matthias Templ | Taschenbuch | 264 S. | Deutsch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838108285 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 101546963

Bibliographic Details

• Title: New Developments in Statistical Disclosure Control and Imputation | Robust Statistics Applied to Official Statistics
• Publisher: Südwestdeutscher Verlag für Hochschulschriften
• Publication Date: 2015
• Language: English
• ISBN 10: 3838108280
• ISBN 13: 9783838108285
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 17D millimeters 220 millimeters width by 150 millimeters height by 17 millimeters depth
• Weight: 411 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The aim of statistical disclosure control is to keep up the required statistical privacy while making data available to the researchers. This can be achieved with the help of minimal modifications of the data without changing the multivariate data structure. In this book the well-developed R package sdcMicro is introduced. With the help of this package it is possible to keep microdata confidential in a very effective way. The concept is thoroughly explained and its application is demonstrated using real-world data. In addition to that, the robustification of disclosure methods is described. Many SDC-methods for microdata developed so far can be influenced by outliers to a great extent resulting in a high loss of information of the perturbed data. Missing values are the second topic of this book. The application of visualisation tools for the analysis of missing values, preceding the choice of an imputation method, is highlighted. In addition to that, new methods for the imputation of composition data are introduced. Due to the linear dependence of the variables from compositional data, reasonalbe imputations can be made by considering the special nature of such data.

About the Author

Matthias Templ, Dr. techn.: Master and PhD studies in TechnicalMathematics at TU WIEN. Since 2006: research and lecture assistent at the Department ofStatistics and Probability Theory, TU WIEN.Since 2004: researcher at Statistics Austria.Project leader for the TU WIEN of the FP7 project AMELI and ofseveral European projects for STAT.