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  • Title: Estimating standard errors in feature network models.
    Author: Frank LE, Heiser WJ.
    Journal: Br J Math Stat Psychol; 2007 May; 60(Pt 1):1-28. PubMed ID: 17535577.
    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.
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