title: Statistical modelling of network panel data: goodness of fit.
abstract: Networks of relationships between individuals influence individual and collective outcomes and are therefore of interest in social psychology, sociology, the health sciences, and other fields. We consider network panel data, a common form of longitudinal network data. In the framework of estimating functions, which includes the method of moments as well as the method of maximum likelihood, we propose score-type tests. The score-type tests share with other score-type tests, including the classic goodness-of-fit test of Pearson, the property that the score-type tests are based on comparing the observed value of a function of the data to values predicted by a model. The score-type tests are most useful in forward model selection and as tests of homogeneity assumptions, and possess substantial computational advantages. We derive one-step estimators which are useful as starting values of parameters in forward model selection and therefore complement the usefulness of the score-type tests. The finite-sample behaviour of the score-type tests is studied by Monte Carlo simulation and compared to t-type tests.
authors.affiliation: Department of Statistics, Penn State University, University Park, 16802, USA. michael.schweinberger@stat.psu.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Schweinberger
mesh_terms: D003198:Computer Simulation / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data; D018803:Models, Economic; D015233:Models, Statistical; D009010:Monte Carlo Method; D010067:Ownership / Q000706:statistics & numerical data; D060756:Social Networking