Activity Number:
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618
- Modeling Extremes in Weather, Networks, and Finance
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Risk Analysis
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Abstract #328785
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Presentation
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Title:
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Are Extreme Value Estimation Methods Useful for Network Data?
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Author(s):
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Tiandong Wang*
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Companies:
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School of Operations Research and Information Engineering
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Keywords:
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Power laws;
Extreme values;
Estimation;
Preferential attachment
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Abstract:
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Preferential attachment is an appealing edge generating mechanism for modeling social networks. It provides both an intuitive description of network growth and an explanation for the observed power laws in degree distributions. However, there are often limitations in fitting parametric network models to data due to the complex nature of real-world networks. In this paper, we consider a semi-parametric estimation approach by looking at only the nodes with large in- or out-degrees of the network. This method examines the tail behavior of both the marginal and joint degree distributions and is based on extreme value theory. We compare it with the existing parametric approaches and demonstrate how it can provide more robust estimates of parameters associated with the network when the data are corrupted or when the model is misspecified.
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Authors who are presenting talks have a * after their name.
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