The effect of microaggregation by individual ranking on the estimation of moments
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Matthias Schmid, 2006. "Estimation of a linear model under microaggregation by individual ranking," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(3), pages 419-438, September.
- Matthias Schmid & Hans Schneeweiss & Helmut Küchenhoff, 2007. "Estimation of a linear regression under microaggregation with the response variable as a sorting variable," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 407-431.
When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:153:y:2009:i:2:p:174-182. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.