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用于更高级别特定群体曲线模型的简化变分推理

Streamlined variational inference for higher level group-specific curve models.

作者信息

Menictas M, Nolan T H, Simpson D G, Wand M P

机构信息

School of Mathematical and Physical Sciences, University of Technology Sydney, Australia.

Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers.

出版信息

Stat Modelling. 2021 Dec 1;21(6):479-519. doi: 10.1177/1471082x20930894. Epub 2020 Aug 21.

Abstract

A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and higher level extensions are analogous. Streamlined variational inference for higher level group-specific curve models is a challenging problem. We confront it by systematically working through two-level and then three-level cases and making use of the higher level sparse matrix infrastructure laid down in Nolan and Wand (2019). A motivation is analysis of data from ultrasound technology for which three-level group-specific curve models are appropriate. Whilst extension to the number of levels exceeding three is not covered explicitly, the pattern established by our systematic approach sheds light on what is required for even higher level group-specific curve models.

摘要

两级组特定曲线模型是指一个组中每个成员的平均响应是感兴趣预测变量的一个单独平滑函数。三级扩展是指一个分组变量嵌套在另一个分组变量中,更高层次的扩展也是类似的。针对更高层次组特定曲线模型的简化变分推断是一个具有挑战性的问题。我们通过系统地处理两级和三级情况,并利用诺兰和万德(2019年)建立的更高层次稀疏矩阵基础设施来应对这一问题。一个动机是对超声技术数据进行分析,对于这些数据,三级组特定曲线模型是合适的。虽然没有明确涵盖超过三级的层次扩展,但我们系统方法所建立的模式为更高层次组特定曲线模型的要求提供了启示。

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