A class of mixtures of dependent tail-free processes

• 成果类型: Article
• 署名作者: Jara, A.; Hanson, T. E.
• 署名单位: Pontificia Universidad Catolica de Chile; University of South Carolina System; University of South Carolina Columbia
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asq082
• 发表日期: 2011
• 页码: 553566
• 关键词: ASYMPTOTIC-BEHAVIOR; inference; distributions; ranges; MODEL

摘要

We propose a class of dependent processes in which density shape is regressed on one or more predictors through conditional tail-free probabilities by using transformed Gaussian processes. A particular linear version of the process is developed in detail. The resulting process is flexible and easy to fit using standard algorithms for generalized linear models. The method is applied to growth curve analysis, evolving univariate random effects distributions in generalized linear mixed models, and median survival modelling with censored data and covariate-dependent errors.