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The University of Southampton
Southampton Statistical Sciences Research Institute

S3RI Seminar - Interlocking directorates in Irish companies using a latent space model for bipartite networks, Professor Nial Friel (University College Dublin) Seminar

S3RI Seminar
Time:
14:00 - 15:00
Date:
15 February 2018
Venue:
Lecture Theatre 8C, Room 8031, Building 54, Mathematical Sciences, University of Southampton, Highfield Campus, SO17 1BJ

For more information regarding this seminar, please email Dr Helen Ogden at H.E.Ogden@southampton.ac.uk .

Event details

We analyze the temporal bipartite network of the leading Irish companies and their directors from 2003 to 2013, encompassing the end of the Celtic Tiger boom and the ensuing financial crisis in 2008. We focus on the evolution of company interlocks, whereby a company director simultaneously sits on two or more boards. We develop a statistical model for this dataset by embedding the positions of companies and directors in a latent space. The temporal evolution of the network is modeled through three levels of Markovian dependence: one on the model parameters, one on the companies’ latent positions, and one on the edges themselves. The model is estimated using Bayesian inference. Our analysis reveals that the level of interlocking, as measured by a contraction of the latent space, increased before and during the crisis, reaching a peak in 2009, and has generally stabilized since then. http://www.pnas.org/content/113/24/6629.full.pdf

 

The seminar will also be available via a live web-cast at
https://cours ecast.soton.ac. uk/Panopto/Page s/Viewer.aspx?i d=2870cda7-1726 -4300-a738-6eb9 dfab5f80

Speaker information

Professor Nial Friel, University College Dublin. My research interests include: Intractable likelihoods -- theory and methodology for statistical models with intractable likelihood function. Statistical network analysis -- Bayesian inference for statistical network models; applications in social network analysis. Spatial statistics -- especially statistical inference for Markov random field models. Bayesian statistics -- model selection; Bayes factors; evidence/marginal likelihood estimation. Markov chain Monte Carlo methods and applications.

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