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• W2962856988 abstract "Social networks and other sparse data sets pose significant challenges for statistical inference, since many standard statistical methods for testing model/data fit are not applicable in such settings. Algebraic statistics offers a theoretically justified approach to goodness-of-fit testing that relies on the theory of Markov bases. Most current practices require the computation of the entire basis, which is infeasible in many practical settings. We present a dynamic approach to explore the fiber of a model, which bypasses this issue, and is based on the combinatorics of hypergraphs arising from the toric algebra structure of log-linear models. We demonstrate the approach on the Holland–Leinhardt $$p_1$$ model for random directed graphs that allows for reciprocation effects." @default.
• W2962856988 created "2019-07-30" @default.
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• W2962856988 date "2016-04-05" @default.
• W2962856988 modified "2023-10-02" @default.
• W2962856988 title "Goodness of fit for log-linear network models: dynamic Markov bases using hypergraphs" @default.
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• W2962856988 doi "https://doi.org/10.1007/s10463-016-0560-2" @default.
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