文章基本信息

标题：Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces
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作者：Samuel I. Berchuck ; Mark Janko ; Felipe A. Medeiros 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2022
卷号：17
期号：2
页码：435-464
DOI：10.1214/20-BA1253
语种：English
出版社：International Society for Bayesian Analysis
摘要：We introduce a Bayesian non-parametric spatial factor analysis model with spatial dependency induced through a prior on factor loadings. For each column of the loadings matrix, spatial dependency is encoded using a probit stick-breaking process (PSBP) and a multiplicative gamma process shrinkage prior is used across columns to adaptively determine the number of latent factors. By encoding spatial information into the loadings matrix, meaningful factors are learned that respect the observed neighborhood dependencies, making them useful for assessing rates over space. Furthermore, the spatial PSBP prior can be used for clustering temporal trends, allowing users to identify regions within the spatial domain with similar temporal trajectories, an important task in many applied settings. In the manuscript, we illustrate the model’s performance in simulated data, but also in two real-world examples: longitudinal monitoring of glaucoma and malaria surveillance across the Peruvian Amazon. The R package spBFA, available on CRAN, implements the method.
关键词：62F15;62G08;62H25;Bayesian non-parametrics;Dimension reduction;factor analysis;probit stick-breaking process;spatiotemporal clustering