Semiparametric Factor and Regression Models for Symmetric
Relational Data
Description
Estimation of the parameters in a model for
symmetric relational data (e.g., the above-diagonal part of a
square matrix), using a model-based eigenvalue decomposition
and regression. Missing data is accommodated, and a posterior
mean for missing data is calculated under the assumption that
the data are missing at random. The marginal distribution of
the relational data can be arbitrary, and is fit with an
ordered probit specification. See Hoff (2007)
for details on the model.