Statistical Connectomics
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DOI: 10.31219/osf.io/ek4n3
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- Edward Scheinerman & Kimberly Tucker, 2010. "Modeling graphs using dot product representations," Computational Statistics, Springer, vol. 25(1), pages 1-16, March.
- Crainiceanu, Ciprian M. & Caffo, Brian S. & Luo, Sheng & Zipunnikov, Vadim M. & Punjabi, Naresh M., 2011. "Population Value Decomposition, a Framework for the Analysis of Image Populations," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 775-790.
- Daniel L. Sussman & Minh Tang & Donniell E. Fishkind & Carey E. Priebe, 2012. "A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1119-1128, September.
- Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Rejoinder: Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1547-1552, October.
- Katharina Eichler & Feng Li & Ashok Litwin-Kumar & Youngser Park & Ingrid Andrade & Casey M. Schneider-Mizell & Timo Saumweber & Annina Huser & Claire Eschbach & Bertram Gerber & Richard D. Fetter & J, 2017. "The complete connectome of a learning and memory centre in an insect brain," Nature, Nature, vol. 548(7666), pages 175-182, August.
- Cape, Joshua & Tang, Minh & Priebe, Carey E., 2019. "On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs," Network Science, Cambridge University Press, vol. 7(3), pages 269-291, September.
- Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
- Edward R. Scheinerman & Kimberly Tucker, 2010. "Modeling graphs using dot product representations," Computational Statistics, Springer, vol. 25(1), pages 1-16, March.
- Zhu, Mu & Ghodsi, Ali, 2006. "Automatic dimensionality selection from the scree plot via the use of profile likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 918-930, November.
- Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
- Joshua T Vogelstein & John M Conroy & Vince Lyzinski & Louis J Podrazik & Steven G Kratzer & Eric T Harley & Donniell E Fishkind & R Jacob Vogelstein & Carey E Priebe, 2015. "Fast Approximate Quadratic Programming for Graph Matching," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
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This paper has been announced in the following NEP Reports:- NEP-NET-2020-09-28 (Network Economics)
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