Abstract
Algebraic sampling methods are a powerful tool to perform hypothesis tests on conditional spaces. We analyse the link of the sampling method introduced in [6] with permutation tests and we exploit this link to build a two-step sampling procedure to perform two-sample comparisons for non-negative discrete exponential families. We thus establish a link between standard permutation and algebraic-statistics-based sampling. The proposed method reduces the dimension of the space on which the MCMC sampling is performed by introducing a second step in which a standard Monte Carlo sampling is performed. The advantages of this dimension reduction are verified through a simulation study, showing that the proposed approach grants convergence in the least time and has the lowest mean squared error.
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Acknowledgments
R. Fontana acknowledges that the present research has been partially supported by MIUR grant Dipartimenti di Eccellenza 2018–2022 (E11G18000350001). The authors thank the anonymous referee for his/her helpful comments.
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Crucinio, F.R., Fontana, R. (2020). Speeding up Algebraic-Based Sampling via Permutations. In: La Rocca, M., Liseo, B., Salmaso, L. (eds) Nonparametric Statistics. ISNPS 2018. Springer Proceedings in Mathematics & Statistics, vol 339. Springer, Cham. https://doi.org/10.1007/978-3-030-57306-5_14
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DOI: https://doi.org/10.1007/978-3-030-57306-5_14
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