Feature screening for network autoregression model

Danyang Huang, Xuening Zhu, Runze Li, Hansheng Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Network analyses are becoming increasingly popular in a wide range disciplines, including social science, finance, and genetics. In practice, it is common to collect numerous covariates along with the response variable. Because the network structure means the responses at different nodes are no longer independent, existing screening methods may not perform well for network data. Therefore, we propose a network-based sure independence screening (NW-SIS) method that explicitly considers the network structure. The strong screening consistency property of the NW-SIS method is rigorously established. Furthermore, we estimate the network effect and establish the √n-consistency of the estimator. The finite-sample performance of the proposed method is assessed using a simulation study and an empirical analysis of a data set from the Chinese stock market.

Original languageEnglish (US)
Pages (from-to)1239-1259
Number of pages21
JournalStatistica Sinica
Volume31
Issue number3
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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