Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/31144 
Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
Discussion Paper No. 416
Verlag: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Zusammenfassung: 
A problem statistical offices are increasingly faced with is guaranteeing confidentiality when releasing microdata sets. One method to provide safe microdata is to reduce the information content of a data set by means of masking procedures. A widely discussed masking procedure is microaggregation, a technique where observations are grouped and replaced with their corresponding group means. However, while reducing the disclosure risk of data file, microaggregation also affects the results of statistical analyses. We focus on the effect of microaggregation on a simple linear model, In a previous paper we have shown how to correct for the aggregation bias of the naive least-squares estimator that occurs when the dependent variable is used to group the data. The present paper deals with the asymptotic variance of the corrected least-squares estimator and with the asymptotic variance of the naive least-squares estimator when either the dependent variable or the regressor is used to group the data. We derive asymptotic confidence intervals for the slope parameter. Furthermore, we show how to test for the significance of the slope parameter by analyzing the effect of microaggregation on the asymptotic power function of the naive t-test.
Schlagwörter: 
Microaggregation
simple linear model
asymptotic variance
t-test
disclosure control
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