Estimation of a linear model under microaggregation by individual ranking |
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Authors: | Matthias Schmid |
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Institution: | (1) Department of Statistics, University of Munich, Ludwigstr. 33, 80539 Munich, Germany |
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Abstract: | Summary Microaggregation by individual ranking is one of themost commonly applied disclosure control techniques for continuous microdata.
The paper studies the effect of microaggregation by individual ranking on the least squares estimation of a multiple linear
regression model. It is shown that the traditional least squares estimates are asymptotically unbiased. Moreover, the least
squares estimates asymptotically have the same variances as the least squares estimates based on the original (non-aggregated)
data. Thus, asymptotically, microaggregation by individual ranking does not result in a loss of efficiency in the least squares
estimation of a multiple linear regression model.
I thank Hans Schneeweiss for very helpful discussions and comments. Financial support from the Deutsche Forschungsgemeinschaft
(German Science Foundation) is gratefully acknowledged. |
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Keywords: | Asymptotic variance consistent estimation disclosure control individual ranking linear model microaggregation |
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