A Differentially Private Bayesian Approach to Replication Analysis

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2022

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Abstract

Replication analysis is widely used in many fields of study. Once a research is published, other researchers will conduct analysis to assess the reliability of the published research. However, what if the data are confidential? In particular, if the data sets used for the studies are confidential, we cannot release the results of replication analyses to any entity without the permission to access the data sets, otherwise it may result in privacy leakage especially when the published study and replication studies are using similar or common data sets. In this paper, we present two methods for replication analysis. We illustrate the properties of our methods by a combination of theoretical analysis and simulation.

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Yang, Chengxin (2022). A Differentially Private Bayesian Approach to Replication Analysis. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/25354.

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