具有全范围信息借用的非参数先验

IF 2.4 2区 数学 Q2 BIOLOGY Biometrika Pub Date : 2023-10-19 DOI:10.1093/biomet/asad063
F Ascolani, B Franzolini, A Lijoi, I Prünster
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引用次数: 0

摘要

跨异构数据的依赖结构建模对贝叶斯推理至关重要,因为它直接影响信息的借用。尽管在过去二十年中取得了广泛的进展,但大多数可用的建议只允许非负相关。我们导出了一类新的非参数依赖先验,它可以诱导任何符号的相关性,从而引入了一种新的更灵活的信息借用思想。这要归功于一个新颖的概念,我们称之为“超联系”,它代表了一种直接而简单的依赖度量。我们研究了模型的先验和后验分布特性,并开发了执行后验推理的算法。在模拟和真实数据上的示例表明,我们的建议在预测和聚类方面优于其他方案。
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Nonparametric priors with full-range borrowing of information
Summary Modelling of the dependence structure across heterogeneous data is crucial for Bayesian inference since it directly impacts the borrowing of information. Despite the extensive advances over the last two decades, most available proposals only allow for nonnegative correlations. We derive a new class of dependent nonparametric priors that can induce correlations of any sign, thus introducing a new and more flexible idea of borrowing of information. This is achieved thanks to a novel concept, which we term hyper-tie, and represents a direct and simple measure of dependence. We investigate prior and posterior distributional properties of the model and develop algorithms to perform posterior inference. Illustrative examples on simulated and real data show that our proposal outperforms alternatives in terms of prediction and clustering.
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来源期刊
Biometrika
Biometrika 生物-生物学
CiteScore
5.50
自引率
3.70%
发文量
56
审稿时长
6-12 weeks
期刊介绍: Biometrika is primarily a journal of statistics in which emphasis is placed on papers containing original theoretical contributions of direct or potential value in applications. From time to time, papers in bordering fields are also published.
期刊最新文献
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