University of Washington - Department of Statistics & Biostatistics & the Center for Statistics and the Social Sciences
Matrix representation techniques have a long history in the analysis of multivariate data, including relational data in which observations are on pairs of individuals or units. In particular, the singular value decomposition of a matrix allows one to represent the relationship between two units as the inner product of a pair of latent characteristic vectors. In this talk I discuss a model-based version of the singular value decomposition which allows for the analysis of a variety of data types, including binary relational data, or â€œsocial networks.â€
One outstanding issue in the use of such models has been the determination of the dimension of this latent space of characteristics. Time permitting, I will show how Bayesian methods can be used to select an appropriate dimension, and how Bayesian model averaging over the dimension can improve upon the predictive power of these models.