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Ph.D. Thesis (2009)

Statistical Models for Social Network Data and Processes 1. Extensions to latent cluster models for social networks 2. ERGM-derived processes under changing network size 3. ERGM-based models and inference for dynamic networks

This work deals with three areas of network modeling. First, in the area of latent space modeling of social networks, it develops and extends latent cluster social network models by adding random effects and providing efficient algorithms for fitting these models. Second, it explores properties of ERGM and ERGM-based models under changing network size, and proposes a way of addressing the problems that arise. Third, in the area of dynamic networks, it proposes and develops a model separating tie formation process from tie dissolution process, facilitating flexible and realistic simulation of dynamic networks. Methods for integrating of adjustments for network size changes into the dynamic models are also developed.
Mark S. Handcock (advisor, reader), Martina Morris (reader), Peter D. Hoff (reader), Steven M. Goodreau, Adrian E. Raftery, and Ira M. Longini, Pedro Domingos (GSR)

Undergraduate Senior Honors Thesis (2003)

The Effect of Integration Cell Size and *In Situ* Target Strength Calculation Method on Acoustic Fish Density Estimates for Alewife Lakes of New York State

Steven J. Schwager, Lars G. Rudstam