Small area estimation with covariates perturbed for disclosure limitation

Silvia Polettini () and Serena Arima ()
Additional contact information
Silvia Polettini: Università di Roma "La Sapienza", Italy
Serena Arima: Università di Roma "La Sapienza", Italy

Statistica, 2015, vol. 75, issue 1, 57-72

Abstract: We exploit the connections between measurement error and data perturbation for disclosure limitation in the context of small area estimation. Our starting point is the model in Ybarra and Lohr (2008), where some of the covariates (all continuous) are measured with error. Using a fully Bayesian approach, we extend the aforementioned model including continuous and categorical auxiliary variables, both possibily perturbed by disclosure limitation methods, with masking distributions fixed according to the assumed protection mechanism. In order to investigate the feasibility of the proposed method, we conduct a simulation study exploring the effect of different post-randomization scenarios on the small area model.

Keywords: Disclosure limitation; Hierarchical Bayesian models; measurement error; PRAM; small area (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:75:y:2015:i:1:p:57-72

Access Statistics for this article

Statistica is currently edited by Department of Statistics, University of Bologna

More articles in Statistica from Department of Statistics, University of Bologna Contact information at EDIRC.
Bibliographic data for series maintained by Giovanna Galatà ().