TY - GEN
T1 - Testing for Network and Spatial Autocorrelation
AU - Lee, Youjin
AU - Ogburn, Elizabeth L.
N1 - Funding Information:
Acknowledgements Youjin Lee and Elizabeth Ogburn were supported by ONR grant N000141512343. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195 and HHSN268201500001I). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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 data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting.
AB - 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 data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting.
KW - Peer effects
KW - Social networks
KW - Spatial autocorrelation
KW - Statistical dependence
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U2 - 10.1007/978-3-030-38965-9_7
DO - 10.1007/978-3-030-38965-9_7
M3 - Conference contribution
AN - SCOPUS:85079167015
SN - 9783030389642
T3 - Springer Proceedings in Complexity
SP - 91
EP - 104
BT - Proceedings of NetSci-X 2020
A2 - Masuda, Naoki
A2 - Goh, Kwang-Il
A2 - Jia, Tao
A2 - Yamanoi, Junichi
A2 - Sayama, Hiroki
PB - Springer
T2 - 6th International School and Conference on Network Science, NetSci-X 2020
Y2 - 20 January 2020 through 23 January 2020
ER -