Authors: Frank, Laurence E.¹; Heiser, Willem J.²

Source: British Journal of Mathematical and Statistical Psychology, Volume 60, Number 1, May 2007 , pp. 1-28(28)

Abstract:

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 employed 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 error is evaluated using Monte Carlo techniques.

Document Type: Research article

DOI: 10.1348/000711005X64240

Affiliations: 1: Department of Methodology and Statistics, Utrecht University, The Netherlands 2: Psychometrics and Research Methodology, Department of Psychology, Leiden University, The Netherlands