Suchergebnisse

Heinz Grohmann -- Hans Schneeweiss -- Heinrich Strecker -- Articles -- Least-Squares Index Numbers -- Stationary State Equations for Personnel Flows in Hierarchical Organizations with Entry at Lowest Rank and Promotion from Within -- Polynomial Entropy -- On the Formation of Statistical Models -- Estimation without Invariance and Hsu's Theorem in Variance Component Models -- Konzepte zur weichen Modellierung sozioökonomischer Zusammenhänge -- Statistical Modelling -- Methoden zur Messung der Schattenwirtschaft -- Searching for the Best Offer when the Distribution of Offers is Truly Unknown -- Fuzzy Weighting in Multiple-Objective Decision Making; the Contributions of Menges and Some New Developments -- Simultaneous Determination of Capital Flows, the Exchange Rate and Interest Rates in the Bonn Forecasting Model 11 -- C-Optimal Decisions with Optimality Properties for Finite Sample Size -- Prediction with Arima Filters -- Exploration and Inference -- Estimating Linear Relations with Errors in the Variables: the Merging of Two Approaches -- Einige Bemerkungen zum Konzept der Kohärenz -- The Estimation of Ratios and Related Quantities -- On a Concept of Asymptotical Risk in Survey Sampling -- Estimation of the Response Variance in Surveys -- Curriculum Vitae Günter Menges -- List of Obituaries -- List of Authors.

Microaggregation by individual ranking (IR) is an important technique for masking confidential econometric data. While being a successful method for controlling the disclosure risk of observations, IR also affects the results of statistical analyses. We conduct a theoretical analysis on the estimation of arbitrary moments from a data set that has been anonymized by means of the IR method. We show that classical moment estimators remain both consistent and asymptotically normal under weak assumptions. This theory provides the justification for applying standard statistical estimation techniques to the anonymized data without having to correct for a possible bias caused by anonymization.

Microaggregation is a popular statistical disclosure control technique for continuous data. The basic principle of microaggregation is to group the observations in a data set and to replace them by their corresponding group means. However, while reducing the disclosure risk of data files, the technique also affects the results of statistical analyses. The paper deals with the impact of microaggregation on a multiple linear regression in continuous variables. We show that parameter estimates are biased if the dependent variable is used to form the groups. Using this result, we develop a consistent estimator that removes the aggregation bias, and derive its asymptotic covariance matrix.