Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11222-011-9312-6 | Statistics and Computing |
Keywords | Field | DocType |
Bayesian model averaging,Data confidentiality,Disclosure limitation,Markov chain Monte Carlo,Regression,Variable selection | Data mining,Probit model,Secure multi-party computation,Bayesian inference,Markov chain Monte Carlo,Feature selection,Computer science,Model selection,Concatenation,Statistics,Linear regression | Journal |
Volume | Issue | ISSN |
23 | 3 | 0960-3174 |
Citations | PageRank | References |
2 | 0.41 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joyee Ghosh | 1 | 9 | 1.09 |
| Jerome P. Reiter | 2 | 216 | 22.12 |