A Simple Class of Bayesian Nonparametric Autoregression Models.
A Simple Class of Bayesian Nonparametric Autoregression Models.
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DOI：
10.1214/13-ba803
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发表时间：
2013-03-01
影响因子：
4.4
通讯作者：
Quintana FA
中科院分区：
文献类型：
--
作者：
Di Lucca MA;Guglielmi A;Müller P;Quintana FA
我们介绍了一个模型的时间序列的连续结果，可以表示为完全非参数回归或密度回归滞后条款。该模型是基于一个依赖的Dirichlet过程之前的一个家庭的随机概率指标的滞后协变量。该方法也扩展到序列的二进制响应。我们讨论的实施和应用的模型之间的等待时间的老忠实间歇泉喷发的序列，并在膀胱肿瘤的复发指标的序列组成的数据集的几个病人。
We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.