Dataset Information

Feature Screening for Network Autoregression Model.

ABSTRACT: Network analysis has drawn great attention in recent years. It is applied to a wide range disciplines. These include but are not limited to social science, finance and genetics. It is typical that one collects abundant covariates along the response variable in practice. Since the network structure makes the responses at different nodes no longer independent, existing screening methods may not perform well for network data. We propose a network-based sure independence screening (NW-SIS) method. This approach explicitly takes the network structure into consideration. The strong screening consistency property of the NW-SIS is rigorously established. We further investigated the estimation of the network effect and establish the n -consistency of the estimator. The finite sample performance of the proposed method is assessed by simulation study and illustrated by an empirical analysis of a dataset from Chinese stock market.

SUBMITTER: Huang D

PROVIDER: S-EPMC8290873 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Feature Screening for Network Autoregression Model.

Huang Danyang D Zhu Xuening X Li Runze R Wang Hansheng H

Statistica Sinica 20210101

Network analysis has drawn great attention in recent years. It is applied to a wide range disciplines. These include but are not limited to social science, finance and genetics. It is typical that one collects abundant covariates along the response variable in practice. Since the network structure makes the responses at different nodes no longer independent, existing screening methods may not perform well for network data. We propose a network-based sure independence screening (NW-SIS) method. T ...[more]

PMID: 34295124

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Dataset Information

Feature Screening for Network Autoregression Model.

Publications

Feature Screening for Network Autoregression Model.

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