期刊
STATISTICAL MODELLING
卷 9, 期 3, 页码 173-197出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1471082X0800900301
关键词
Box-Cox transformation; data augmentation; data coarsening; latent Gaussian model; maximum indicant model; MCMC; missing data; mixed response models; multilevel; multiple imputation; multivariate; normalising transformations; partially known values; prediction; prior-informed imputation; probit model
资金
- Economic and Social Research Council, UK [RES-000-23-0140, PTA 035-250027]
- Medical Research Council, UK [60600599]
- Economic and Social Research Council [ES/F031904/1, RES-000-23-0140-A, ES/G026300/1] Funding Source: researchfish
- ESRC [ES/F031904/1, ES/G026300/1] Funding Source: UKRI
We build upon the existing literature to formulate a class of models for multivariate mixtures of Gaussian, ordered or unordered categorical responses and continuous distributions that are not Gaussian, each of which can be defined at any level of a multilevel data hierarchy. We describe a Markov chain Monte Carlo algorithm for fitting such models. We show how this unifies a number of disparate problems, including partially observed data and missing data in generalized linear modelling. The two-level model is considered in detail with worked examples of applications to a prediction problem and to multiple imputation for missing data. We conclude with a discussion outlining possible extensions and connections in the literature. Software for estimating the models is freely available.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据