Categorical data regression diagnostics for remote access servers

Abstract

Owing to the growing concerns over data confidentiality, many national statistical agencies are considering remote access servers to disseminate data to the public. With remote servers, users submit requests for output from statistical models fit using the collected data, but they are not allowed access to the data. Remote servers also should enable users to check the fit of their models; however, standard diagnostics like residuals or influence statistics can disclose individual data values. In this article, we present diagnostics for categorical data regressions that can be safely and usefully employed in remote servers. We illustrate the diagnostics with simulation studies.

Keywords:

• Categorical data
• Confidentiality
• Diagnostic
• Disclosure
• Regression
• Remote access

Acknowledgements

This research was supported by NSF grant EIA-0131884 to the National Institute of Statistical Sciences.