Zobrazeno 1 - 10
of 150
pro vyhledávání: '"Jerome P. Reiter"'
Autor:
Jerome P. Reiter
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 8, Iss 1 (2018)
Steve Fienberg had an enormous influence on how I think about statistical science and a huge impact on my career. Steve's research is of course legendary; he made fundamental contributions to Bayesian inference, categorical data analysis, disclosure
Externí odkaz:
https://doaj.org/article/8e423bc030904264b8a2b8902580eaa0
Autor:
Jerome P. Reiter
Publikováno v:
Revstat Statistical Journal, Vol 16, Iss 2 (2018)
We present a joint modeling approach for multiple imputation of missing continuous and categorical variables using Bayesian mixture models. The approach extends the idea of focused clustering, in which one separates variables into two sets before est
Externí odkaz:
https://doaj.org/article/93a84e61e53040cf8570ad7934900661
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 6, Iss 1 (2014)
Agencies seeking to disseminate public use microdata, i.e., data on individual records, can replace confidential values with multiple draws from statistical models estimated with the collected data. We present a famework for evaluating disclosure ris
Externí odkaz:
https://doaj.org/article/3fe860b20d69467cb3750d71ee1117b0
Autor:
David R. McClure, Jerome P. Reiter
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 4, Iss 1 (2012)
When releasing individual-level data to the public, statistical agencies typically alter data values to protect the confidentiality of individuals’ identities and sensitive attributes. When data undergo substantial perturbation, secondary data anal
Externí odkaz:
https://doaj.org/article/cc0c22ae342c4364844fe42c367b670e
Autor:
Jerome P. Reiter
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 3, Iss 2 (2011)
Externí odkaz:
https://doaj.org/article/c2f8a79e494347bebd5df41967413a65
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 2, Iss 2 (2011)
Several statistical agencies use, or are considering the use of, multiple imputation to limit the risk of disclosing respondents' identities or sensitive attributes in public use files. For example, agencies can release partially synthetic datasets,
Externí odkaz:
https://doaj.org/article/95d84aba58a74083b5a7fc51c032160d
Autor:
Jerome P. Reiter
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 1, Iss 2 (2010)
Externí odkaz:
https://doaj.org/article/6b4ca6a436eb4971aa6d636cd6fc7cfc
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 1, Iss 1 (2009)
When releasing microdata to the public, data disseminators typically alter the original data to protect the confidentiality of database subjects' identities and sensitive attributes. However, such alteration negatively impacts the utility (quality) o
Externí odkaz:
https://doaj.org/article/0c6d8ad01b934ef69ed7bb3375a5ccf1
Autor:
Jerome P. Reiter, Robin Mitra
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 1, Iss 1 (2009)
To limit disclosures, statistical agencies and other data disseminators can release partially synthetic, public use microdata sets. These comprise the units originally surveyed; but some collected values, for example, sensitive values at high risk of
Externí odkaz:
https://doaj.org/article/3e43326ad4b04b6e888ab0ddb85bdcb8
Autor:
Zeki Kazan, Jerome P. Reiter
Publikováno v:
Statistica Sinica.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c87cb82aee04bedd7a1e76798166a02
https://doi.org/10.5705/ss.202022.0187
https://doi.org/10.5705/ss.202022.0187