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Published in Annals of Statistics, 2019
Recommended citation: Schweinberger, M., and Stewart, J. "Concentration and consistency results for canonical and curved exponential-family models of random graphs" Annals of Statistics, to appear (2019). [PDF] http://jrstew.github.io/files/aos.pdf
Published in In preparation, 2019
Recommended citation: Stewart, J. and Schweinberger, M. "Generalized beta-models with dependent edges and parameter vectors of increasing dimension", in preparation (2019).
Published in In preparation, 2019
Recommended citation: Fujimoto, K., Stewart, J., Westherim, J., Brauchle, N., Hallmark, C., Benbow, N., DAquila, R., Schneider, J. A., and Schweinberger, M. "Characterizing hotspot HIV transmission networks", in preparation (2019).
Published in Social Networks, 2019
Recommended citation: Stewart, J., Schweinberger, M., Morris, M., and Bojanowski, M. "Multilevel network data facilitate statistical inference for curved ergms with geometrically weighted terms." Social Networks. 59 (2019), 98-119. [PDF] http://jrstew.github.io/files/social_networks.pdf
Published in The American Journal of Human Genetics, 2019
Recommended citation: Campbell, I. M., Stewart, J. R., James, R. A., Lupski, J. R., Stankiewicz, P., Oloffson, P., and Shaw, C. A. "Parent of origin, mosaicism, and recurrence risk: Probabilistic modeling explains the broken symmetry of transmission genetics" The American Journal of Human Genetics, 95 (4) (2014), 345-359. [PDF] http://jrstew.github.io/files/ajhg.pdf
Published in Statistical Science, 2019
Recommended citation: Schweinberger, M., Krivitsky, P. N., Butts, C. T., and Stewart, J. "Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite-Population Scenarios " Statistical Science, to appear (2019). [PDF] http://jrstew.github.io/files/stat_science.pdf
Published:
In social networks, ties depend on other ties owing to social processes that give rise to transitive closure and other forms of closure. While in small social networks ties can depend on all other ties, in large social networks ties do not depend on all other ties but depend on a subset of other ties, because social networks are more local than global in nature. A simple class of models that respects the local nature of social networks assumes that actors are divided into subsets and ties depend on other ties within the same subset, but do not depend on ties outside of the subset. If the subsets are observed, the network is a special case of a multilevel network. Otherwise, the subsets have to be learned from observed social networks.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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