Here - Tilburg University

Author and presenter
Lospinoso, Joshua A.; Dept. of Statistics, University of Oxford
Title
Joint inference on informant accuracy and social network dynamics
Abstract
To date, models for social network dynamics have been formulated
(perhaps implicitly) in such a manner that complete trust is bestowed upon the
observed data; the observed data panels are taken for the true state of
relationships and data augmentation proceeds by constructing chains that could
have connected them. This trend of placing complete trust in the data follows
the general social networks literature. However, the extensive literature on
informant accuracy suggests that placing such trust in the observed data as a
representation of the true state of relations may be a tenuous proposition.
In this talk, a flexible class of models is considered which relaxes the strict
trustin observed network data.
This intermediate model for informant accuracy can represent basic noise with
some false positive rate and some false negative rate and be elaborated by
random and fixed effects models. Bayesian ideas can be leveraged, as the notion
of genuine prior belief about informant accuracy may be particularly appropriate.
Further, the exponential random graph (ERG) family of models can be naturally
employed for this purpose. In this sense, the model is a generalization of the
Stochastic Actor Oriented Models of Snijders (2001) which permits joint
inference on the social network dynamics and on the informant accuracy.