Title
Model Diagnostics for Remote Access Regression Servers
Abstract
To protect public-use microdata, one approach is not to allow users access to the microdata. Instead, users submit analyses to a remote computer that reports back basic output from the fitted model, such as coefficients and standard errors. To be most useful, this remote server also should provide some way for users to check the fit of their models, without disclosing actual data values. This paper discusses regression diagnostics for remote servers. The proposal is to release synthetic diagnostics—i.e. simulated values of residuals and dependent and independent variables–constructed to mimic the relationships among the real-data residuals and independent variables. Using simulations, it is shown that the proposed synthetic diagnostics can reveal model inadequacies without substantial increase in the risk of disclosures. This approach also can be used to develop remote server diagnostics for generalized linear models.
Year
DOI
Venue
2003
10.1023/A:1025623108012
Statistics and Computing
Keywords
DocType
Volume
confidentiality,diagnostics,disclosure,regression,remote access,synthetic data
Journal
13
Issue
ISSN
Citations 
4
1573-1375
19
PageRank 
References 
Authors
2.17
4
1
Name
Order
Citations
PageRank
Jerome P. Reiter121622.12