Journal article
Secure Bayesian model averaging for horizontally partitioned data
Statistics and Computing, Vol.23(3), pp.311-322
05/2013
DOI: 10.1007/s11222-011-9312-6
Abstract
When multiple data owners possess records on different subjects with the same set of attributes—known as horizontally partitioned data—the data owners can improve analyses by concatenating their databases. However, concatenation of data may be infeasible because of confidentiality concerns. In such settings, the data owners can use secure computation techniques to obtain the results of certain analyses on the integrated database without sharing individual records. We present secure computation protocols for Bayesian model averaging and model selection for both linear regression and probit regression. Using simulations based on genuine data, we illustrate the approach for probit regression, and show that it can provide reasonable model selection outputs.
Details
- Title: Subtitle
- Secure Bayesian model averaging for horizontally partitioned data
- Creators
- Joyee Ghosh - Department of Statistics and Actuarial Science The University of Iowa Iowa City USAJerome Reiter - Department of Statistical Science Duke University Durham USA
- Resource Type
- Journal article
- Publication Details
- Statistics and Computing, Vol.23(3), pp.311-322
- Publisher
- Springer US; Boston
- DOI
- 10.1007/s11222-011-9312-6
- ISSN
- 0960-3174
- eISSN
- 1573-1375
- Language
- English
- Date published
- 05/2013
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985957702771
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