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A Bayesian Synthesis Approach to Data Fusion Using Augmented Data-Dependent Priors


Abstract The process of combining data is one in which information from disjoint datasets sharing at least a number of common variables is merged. This process is commonly referred to as data fusion, with the main objective of creating a new dataset permitting more flexible analyses than the separate analysis of each individual dataset. Many data fusion methods have been proposed in the literature, although most utilize the frequentist framework. This dissertation investigates a new approach called Bayesian Synthesis in which information obtained from one dataset acts as priors for the next analysis. This process continues sequentially until a single posterior distribution is created using all available data. These informative augmented data-depende... (more)
Created Date 2017
Contributor Marcoulides, Katerina Marie (Author) / Grimm, Kevin (Advisor) / Levy, Roy (Advisor) / Mackinnon, David (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Subject Quantitative psychology / Bayesian Synthesis / Data Fusion / Data Integration / Longitudinal Growth Modeling
Type Doctoral Dissertation
Extent 137 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Psychology 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis