High-Dimensional Tail Index Regression

Yuya Sasaki, Jing Tao and Yulong Wang

Papers from arXiv.org

Abstract: 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).

Date: 2024-03, Revised 2026-01
New Economics Papers: this item is included in nep-ecm and nep-pay
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