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Estimating standard errors in feature network models
Author(s) -
Frank Laurence E.,
Heiser Willem J.
Publication year - 2007
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711005x64240
Subject(s) - standard error , monte carlo method , univariate , computer science , linear regression , feature (linguistics) , mathematics , multidimensional scaling , data mining , algorithm , statistics , multivariate statistics , philosophy , linguistics
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.