Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation

• 成果类型: Article
• 署名作者: Hall, P.; Johnstone, I. M.; Ormerod, J. T.; Wand, M. P.; Yu, J. C. F.
• 署名单位: University of Melbourne; Stanford University; University of Sydney; University of Technology Sydney
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1665529
• 发表日期: 2020
• 页码: 1902-1916
• 关键词: inference

摘要

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.