Testing for Nodal Dependence in Relational Data Matrices

Alexander Volfovsky and Peter D. Hoff

Journal of the American Statistical Association, 2015, vol. 110, issue 511, 1037-1046

Abstract: Relational data are often represented as a square matrix, the entries of which record the relationships between pairs of objects. Many statistical methods for the analysis of such data assume some degree of similarity or dependence between objects in terms of the way they relate to each other. However, formal tests for such dependence have not been developed. We provide a test for such dependence using the framework of the matrix normal model, a type of multivariate normal distribution parameterized in terms of row- and column-specific covariance matrices. We develop a likelihood ratio test (LRT) for row and column dependence based on the observation of a single relational data matrix. We obtain a reference distribution for the LRT statistic, thereby providing an exact test for the presence of row or column correlations in a square relational data matrix. Additionally, we provide extensions of the test to accommodate common features of such data, such as undefined diagonal entries, a nonzero mean, multiple observations, and deviations from normality. Supplementary materials for this article are available online.

Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/01621459.2014.965777

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