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Three Essays on Correlated Binary Outcomes: Detection and Appropriate Models


Abstract Correlation is common in many types of data, including those collected through longitudinal studies or in a hierarchical structure. In the case of clustering, or repeated measurements, there is inherent correlation between observations within the same group, or between observations obtained on the same subject. Longitudinal studies also introduce association between the covariates and the outcomes across time. When multiple outcomes are of interest, association may exist between the various models. These correlations can lead to issues in model fitting and inference if not properly accounted for. This dissertation presents three papers discussing appropriate methods to properly consider different types of association. The first paper introd... (more)
Created Date 2018
Contributor Irimata, Kyle (Author) / Wilson, Jeffrey R (Advisor) / Broatch, Jennifer (Committee member) / Kamarianakis, Ioannis (Committee member) / Kao, Ming-Hung (Committee member) / Reiser, Mark (Committee member) / Arizona State University (Publisher)
Subject Statistics / Correlation / Generalized Method of Moments / Hierarchical Data / Logistic Regression
Type Doctoral Dissertation
Extent 100 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Statistics 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis