A blockwise network autoregressive model with application for fraud detection

Bofei Xiao, Bo Lei, Wei Lan and Bin Guo ()
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Bofei Xiao: Southwestern University of Finance and Economics
Bo Lei: Southwestern University of Finance and Economics
Wei Lan: Southwestern University of Finance and Economics
Bin Guo: Southwestern University of Finance and Economics

Annals of the Institute of Statistical Mathematics, 2022, vol. 74, issue 6, No 1, 1043-1065

Abstract: Abstract This paper proposes a blockwise network autoregressive (BWNAR) model by grouping nodes in the network into nonoverlapping blocks to adapt networks with blockwise structures. Before modeling, we employ the pseudo likelihood ratio criterion (pseudo-LR) together with the standard spectral clustering approach and a binary segmentation method developed by Ma et al. (Journal of Machine Learning Research, 22, 1–63, 2021) to estimate the number of blocks and their memberships, respectively. Then, we acquire the consistency and asymptotic normality of the estimator of influence parameters by the quasi-maximum likelihood estimation method without imposing any distribution assumptions. In addition, a novel likelihood ratio test statistic is proposed to verify the heterogeneity of the influencing parameters. The performance and usefulness of the model are assessed through simulations and an empirical example of the detection of fraud in financial transactions, respectively.

Keywords: Blockwise network autoregressive model; Blockwise structure; Community detection; Likelihood ratio test; Quasi-maximum likelihood estimation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10463-022-00822-w

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