A bayesian semiparametric latent variable model for mixed responses
A bayesian semiparametric latent variable model for mixed responses
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DOI：
10.1007/s11336-007-9010-7
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发表时间：
2007-09-01
期刊：
影响因子：
3
通讯作者：
Raach, Alexander
中科院分区：
文献类型：
--
作者：
Fahrmeir, Ludwig;Raach, Alexander
在本文中，我们介绍了一个潜变量模型（LVM）的混合顺序和连续响应，协变量对连续潜变量的影响建模通过一个灵活的半参数高斯回归模型。我们扩展现有的LVMs与通常的线性协变量的影响，包括非参数组件的非线性效应的连续协变量和与其他协变量的相互作用，以及空间效应。完全贝叶斯建模是基于惩罚样条和马尔可夫随机场先验，并通过计算效率高的马尔可夫链蒙特卡罗（MCMC）方法进行。我们应用我们的方法，德国社会科学调查，激励我们的方法发展。
In this paper we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semi-parametric Gaussian regression model. We extend existing LVMs with the usual linear covariate effects by including nonparametric components for nonlinear effects of continuous covariates and interactions with other covariates as well as spatial effects. Full Bayesian modelling is based on penalized spline and Markov random field priors and is performed by computationally efficient Markov chain Monte Carlo (MCMC) methods. We apply our approach to a German social science survey which motivated our methodological development.