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dc.contributor.advisor Mukherjee, Sayan en_US
dc.contributor.author Yang, Xi en_US
dc.date.accessioned 2016-06-06T16:50:57Z
dc.date.available 2016-06-06T16:50:57Z
dc.date.issued 2016 en_US
dc.identifier.uri http://hdl.handle.net/10161/12357
dc.description Thesis en_US
dc.description.abstract <p>Extensive investigation has been conducted on network data, especially weighted network in the form of symmetric matrices with discrete count entries. Motivated by statistical inference on multi-view weighted network structure, this paper proposes a Poisson-Gamma latent factor model, not only separating view-shared and view-specific spaces but also achieving reduced dimensionality. A multiplicative gamma process shrinkage prior is implemented to avoid over parameterization and efficient full conditional conjugate posterior for Gibbs sampling is accomplished. By the accommodating of view-shared and view-specific parameters, flexible adaptability is provided according to the extents of similarity across view-specific space. Accuracy and efficiency are tested by simulated experiment. An application on real soccer network data is also proposed to illustrate the model.</p> en_US
dc.subject Statistics en_US
dc.subject Multi-View en_US
dc.subject Network en_US
dc.subject Weighted en_US
dc.title Multi-View Weighted Network en_US
dc.type Thesis en_US
dc.department Statistical and Economic Modeling en_US

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