Network regression and supervised centrality estimation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
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| _version_ | 1866913706028302336 |
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| author | Cai, Junhui Yang, Dan Chen, Ran Zhu, Wu Shen, Haipeng Zhao, Linda |
| author_facet | Cai, Junhui Yang, Dan Chen, Ran Zhu, Wu Shen, Haipeng Zhao, Linda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_12921 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Network regression and supervised centrality estimation Cai, Junhui Yang, Dan Chen, Ran Zhu, Wu Shen, Haipeng Zhao, Linda Econometrics Social and Information Networks Methodology 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. |
| title | Network regression and supervised centrality estimation |
| topic | Econometrics Social and Information Networks Methodology |
| url | https://arxiv.org/abs/2111.12921 |