Latent Stick-Breaking Processes

• 成果类型: Article
• 署名作者: Rodriguez, Abel; Dunson, David B.; Gelfand, Alan E.
• 署名单位: University of California System; University of California Santa Cruz; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2010.tm08241
• 发表日期: 2010
• 页码: 647-659
• 关键词: bayesian-analysis

摘要

We develop a model for stochastic processes with random marginal distributions. Our model relies on a stick-breaking construction for the marginal distribution of the process, and introduces dependence across locations by using a latent Gaussian copula model as the mechanism for selecting the atoms. The resulting latent stick-breaking process (LaSBP) induces a random partition of the index space, with points closer in space having a higher probability of being in the same cluster. We develop an efficient and straightforward Markov chain Monte Carlo (MCMC) algorithm for computation and discuss applications in financial econometrics and ecology. This article has supplementary material online.