Bayesian semiparametric regression analysis of multicategorical time-space data
Bayesian semiparametric regression analysis of multicategorical time-space data
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DOI：
10.1023/a:1017904118167
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发表时间：
2001-03-01
影响因子：
1
通讯作者：
Lang, S
中科院分区：
文献类型：
--
作者：
Fahrmeir, L;Lang, S
提出了一种基于马尔可夫随机场先验的统一半参数贝叶斯方法，用于分析多类响应变量对时间、空间和协变量的依赖性。一般模型扩展动态，或状态空间，模型的分类时间序列和纵向数据，包括空间效应以及非线性效应的测量协变量的灵活的半参数形式。趋势和季节成分，不同类型的协变量和空间效应都在同一个一般框架内处理，通过分配适当的先验不同的形式和程度的平滑。推理是完全贝叶斯的，并使用MCMC技术进行后验分析。在本文中的方法是基于潜在的半参数效用模型，是特别有用的概率模型。失业数据和森林损害调查的应用程序的方法进行说明。
We present a unified semiparametric Bayesian approach based on Markov random field priors for analyzing the dependence of multicategorical response variables on time, space and further covariates. The general model extends dynamic, or state space, models for categorical time series and longitudinal data by including spatial effects as well as nonlinear effects of metrical covariates in flexible semiparametric form. Trend and seasonal components, different types of covariates and spatial effects are ail treated within the same general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference is fully Bayesian and uses MCMC techniques for posterior analysis. The approach in this paper is based on latent semiparametric utility models and is particularly useful for probit models. The methods are illustrated by applications to unemployment data and a forest damage survey.