Fast and Accurate Binary Response Mixed Model Analysis Via Expectation Propagation.

Fast and Accurate Binary Response Mixed Model Analysis Via Expectation Propagation.
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通过期望传播的快速准确的二元响应混合模型分析。

DOI:
10.1080/01621459.2019.1665529
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发表时间:
2020
影响因子:
3.7
通讯作者:
--
中科院分区:
数学1区
文献类型:
--
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期望传播是统计推断问题中积分近似的一种通用方法。其文献主要涉及贝叶斯推理场景。然而,期望传播也可以用来近似在频率统计推断中出现的积分。我们专注于二进制响应混合模型的基于似然的推理,并表明,快速,准确的无平方根推理可以实现的概率链接的情况下,多变量随机效应和更高层次的嵌套。该方法是支持渐近计算中,期望传播被认为是提供一致的估计的精确似然面。数值研究表明,快速,高精度和可扩展的方法的二元混合模式分析的可用性。本文的补充材料可在网上查阅。
Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to approximate integrals arising in frequentist statistical inference. We focus on likelihood-based inference for binary response mixed models and show that fast and accurate quadrature-free inference can be realized for the probit link case with multivariate random effects and higher levels of nesting. The approach is supported by asymptotic calculations in which expectation propagation is seen to provide consistent estimation of the exact likelihood surface. Numerical studies reveal the availability of fast, highly accurate and scalable methodology for binary mixed model analysis. Supplementary materials for this article are available online.
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