Feature network models for proximity data

Feature Network Models are graphical structures that represent proximity data in a discrete space while using the same formalism that is the basis of least squares methods used in multidimensional scaling. Existing methods to derive a network model from empirical data only give the best fitting network and yield no standard errors for the parameter estimates. The additivity properties of networks make it possible to consider the model as a univariate (multiple) linear regression problem with positivity restrictions on the parameters. In the present study, both theoretical and empirical standard errors are obtained for the constrained regression parameters of a network model with known features. The performance of both types of standard errors are evaluated using Monte Carlo techniques.