Estimating standard errors in feature network models.
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.
Frank
Laurence E
LE
Department of Methodology and Statistics, Utrecht University, The Netherlands. l.e.frank@uu.nl
Heiser
Willem J
WJ
D003196:Computer Graphics; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D010364:Pattern Recognition, Visual; D010699:Phonation; D010700:Phonetics; D014956:Writing