Estimating the correlation in network disturbance models
| Autor: | Gesine Reinert, A. D. Barbour |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050208 finance
Control and Optimization Dependency (UML) Disturbance (geology) Computer Networks and Communications Applied Mathematics Maximum likelihood 05 social sciences Estimator Mathematics - Statistics Theory Statistics Theory (math.ST) Management Science and Operations Research Sketch Correlation Computational Mathematics 0502 economics and business FOS: Mathematics 91D30 91Cxx 62P25 62J05 Applied mathematics 050207 economics Mathematics |
| Zdroj: | Journal of Complex Networks. 9 |
| ISSN: | 2051-1329 2051-1310 |
| DOI: | 10.1093/comnet/cnab028 |
| Popis: | The Network Disturbance Model of Doreian (1989) expresses the dependency between observations taken at the vertices of a network by modelling the correlation between neighbouring vertices, using a single correlation parameter $\rho$. It has been observed that estimation of $\rho$ in dense graphs, using the method of Maximum Likelihood, leads to results that can be both biased and very unstable. In this paper, we sketch why this is the case, showing that the variability cannot be avoided, no matter how large the network. We also propose a more intuitive estimator of $\rho$, which shows little bias. The related Network Effects Model is briefly discussed. Comment: 20 pages, 1 Figure; updated version with more details |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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