High-Dimensional Tail Index Regression
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914258891046912 |
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| author | Sasaki, Yuya Tao, Jing Wang, Yulong |
| author_facet | Sasaki, Yuya Tao, Jing Wang, Yulong |
| contents | Motivated by the empirical observation of power-law distributions in the credits (e.g., ``likes'') of viral posts in social media, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we propose a regularized estimator, establish its consistency, and derive its convergence rate. Second, we debias the regularized estimator to facilitate inference and prove its asymptotic normality. Simulation studies corroborate our theoretical findings. We apply these methods to the text analysis of viral posts on X (formerly Twitter). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01318 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-Dimensional Tail Index Regression Sasaki, Yuya Tao, Jing Wang, Yulong Machine Learning Econometrics Motivated by the empirical observation of power-law distributions in the credits (e.g., ``likes'') of viral posts in social media, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we propose a regularized estimator, establish its consistency, and derive its convergence rate. Second, we debias the regularized estimator to facilitate inference and prove its asymptotic normality. Simulation studies corroborate our theoretical findings. We apply these methods to the text analysis of viral posts on X (formerly Twitter). |
| title | High-Dimensional Tail Index Regression |
| topic | Machine Learning Econometrics |
| url | https://arxiv.org/abs/2403.01318 |