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Article . 2022 . Peer-reviewed
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Article . 2022
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Statistical inference on group Rasch mixture network models

Authors: Yuhang Long; Tao Huang;

Statistical inference on group Rasch mixture network models

Abstract

In a two‐mode network, the nodes are divided into two types (primary nodes and secondary nodes), and connections exist only between nodes of different types. In reality, in such a two‐mode network, one‐mode network connections may also exist among primary nodes, and these two kinds of networks are usually not independent and coexistent. In this paper, we first propose a group Rasch mixture network model that focuses on the connections between primary nodes and secondary nodes, while incorporating the group structure and linkage information of primary nodes. We then develop a modified expectation–maximization algorithm to estimate the proposed model with a λ‐BIC method for selecting the tuning parameter. Additionally, we provide a likelihood‐ratio test statistic to examine whether the two kinds of networks are independent and implement the leave‐one‐out method to construct a network prediction rule. Finally, we establish asymptotic results and demonstrate the numerical performance of the proposed methods using both simulations and the Last.fm dataset.

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Keywords

mixture model, Statistics, expectation-maximization algorithm, likelihood-ratio test, Rasch model

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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