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    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Biometrika Vol. 107, No. 4 ( 2020-12-01), p. 1005-1012
    In: Biometrika, Oxford University Press (OUP), Vol. 107, No. 4 ( 2020-12-01), p. 1005-1012
    Abstract: Classification with high-dimensional data is of widespread interest and often involves dealing with imbalanced data. Bayesian classification approaches are hampered by the fact that current Markov chain Monte Carlo algorithms for posterior computation become inefficient as the number $p$ of predictors or the number $n$ of subjects to classify gets large, because of the increasing computational time per step and worsening mixing rates. One strategy is to employ a gradient-based sampler to improve mixing while using data subsamples to reduce the per-step computational complexity. However, the usual subsampling breaks down when applied to imbalanced data. Instead, we generalize piecewise-deterministic Markov chain Monte Carlo algorithms to include importance-weighted and mini-batch subsampling. These maintain the correct stationary distribution with arbitrarily small subsamples and substantially outperform current competitors. We provide theoretical support for the proposed approach and demonstrate its performance gains in simulated data examples and an application to cancer data.
    Type of Medium: Online Resource
    ISSN: 0006-3444 , 1464-3510
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    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1119-8
    detail.hit.zdb_id: 1470319-1
    SSG: 12
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