Network regression and supervised centrality estimation

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Tác giả: Junhui Cai, Ran Chen, Haipeng Shen, Dan Yang, Linda Zhao, Wu Zhu

Ngôn ngữ: eng

Ký hiệu phân loại: 511.4 Approximations formerly also 513.24 and expansions

Thông tin xuất bản: 2021

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Bộ sưu tập: Metadata

ID: 168259

The centrality in a network is often used to measure nodes' importance and model network effects on a certain outcome. Empirical studies widely adopt a two-stage procedure, which first estimates the centrality from the observed noisy network and then infers the network effect from the estimated centrality, even though it lacks theoretical understanding. We propose a unified modeling framework to study the properties of centrality estimation and inference and the subsequent network regression analysis with noisy network observations. Furthermore, we propose a supervised centrality estimation methodology, which aims to simultaneously estimate both centrality and network effect. We showcase the advantages of our method compared with the two-stage method both theoretically and numerically via extensive simulations and a case study in predicting currency risk premiums from the global trade network.
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