High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media

arXiv:2403.01318v1 Announce Type: cross
Abstract: Motivated by the empirical power law of the distributions of credits (e.g., the number of "likes") of viral posts in social media, we introduce the high-dimensional tail index regression and methods of estimation and inference for its parameters. We propose a regularized estimator, establish its consistency, and derive its convergence rate. To conduct inference, we propose to debias the regularized estimate, and establish the asymptotic normality of the debiased estimator. Simulation studies support our theory. …

abstract application arxiv cs.lg econ.em index inference law media parameters power regression social social media stat.ml text type viral