Testing for Network and Spatial Autocorrelation

Research output: Contribution to journalArticlepeer-review

Abstract

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in network, rather than spatial, data, motivated by applications in social network data. We demonstrate that existing tests for autocorrelation in spatial data for continuous variables and our new test for categorical variables can both be used in the network setting.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Oct 9 2017

Keywords

  • Peer effects
  • Social networks
  • Spatial autocorrelation
  • Statistical dependence

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Testing for Network and Spatial Autocorrelation'. Together they form a unique fingerprint.

Cite this