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Parallel inference for big data with the group Bayesian method

Author

Listed:
  • Guangbao Guo

    (Shandong University of Technology)

  • Guoqi Qian

    (The University of Melbourne)

  • Lu Lin

    (Shandong University)

  • Wei Shao

    (Qufu Normal University)

Abstract

In recent years, big datasets are often split into several subsets due to the storage requirements. We propose a parallel group Bayesian method for statistical inference in sparse big data. This method improves the existing methods in two aspects: the total datasets are also split into a data subset sequence and the parameter vector is divided into several sub-vectors. Besides, we add a weight sequence to optimize the sub-estimators when each of them has a different covariance matrix. We obtain several theoretical properties of the estimator. The results of numerical simulations show that our method is consistent with the theoretical results and is more effective than classic Markov chain Monte Carlo methods.

Suggested Citation

  • Guangbao Guo & Guoqi Qian & Lu Lin & Wei Shao, 2021. "Parallel inference for big data with the group Bayesian method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 225-243, February.
    • Handle: RePEc:spr:metrik:v:84:y:2021:i:2:d:10.1007_s00184-020-00784-0
    DOI: 10.1007/s00184-020-00784-0
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    References listed on IDEAS

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    1. Matias Quiroz & Robert Kohn & Mattias Villani & Minh-Ngoc Tran, 2019. "Speeding Up MCMC by Efficient Data Subsampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 831-843, April.
    2. Faming Liang & Qifan Song & Kai Yu, 2013. "Bayesian Subset Modeling for High-Dimensional Generalized Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 589-606, June.
    3. repec:dau:papers:123456789/5671 is not listed on IDEAS
    4. Michael I. Jordan & Jason D. Lee & Yun Yang, 2019. "Communication-Efficient Distributed Statistical Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 668-681, April.
    5. Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
    6. Denwood, Matthew J., 2016. "runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i09).
    7. Ping Zeng & Xiang Zhou, 2017. "Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    8. Qifan Song & Faming Liang, 2015. "A split-and-merge Bayesian variable selection approach for ultrahigh dimensional regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(5), pages 947-972, November.
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