Assessing Local Model Adequacy in Bayesian Hierarchical Models Using the Partitioned Deviance Information Criterion.
Assessing Local Model Adequacy in Bayesian Hierarchical Models Using the Partitioned Deviance Information Criterion.
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DOI：
10.1016/j.csda.2010.01.025
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发表时间：
2010-06-01
影响因子：
1.8
通讯作者：
Waller LA
中科院分区：
文献类型：
--
作者：
Wheeler DC;Hickson DA;Waller LA
许多诊断工具和拟合优度度量，如赤池信息准则（AIC）和贝叶斯偏差信息准则（DIC），可用于评估线性回归模型的整体充分性。此外，可视化地评估模型的充分性已成为任何回归分析的重要组成部分。在本文中，我们重点考虑了局部DIC度量在模型选择和拟合优度评价中的空间考虑。我们将DIC划分为局部DIC、杠杆和偏差残差，以评估贝叶斯框架中单个观测值和组观测值的局部模型拟合和影响。我们使用局部DIC的可视化和模型之间局部DIC的差异来帮助模型选择，并可视化添加协变量或模型参数的全局和局部影响。我们利用1989-1993年期间卢旺达布塔雷省孕妇的艾滋病毒流行率数据，使用一系列线性模型规格，从全局效应到空间变化系数模型，以及一组与性行为相关的协变量，证明了当地DIC在评估模型充分性方面的效用。应用诊断可视化方法的结果包括更精细的模型选择和对应用于数据的模型的更好理解。
Many diagnostic tools and goodness-of-fit measures, such as the Akaike information criterion (AIC) and the Bayesian deviance information criterion (DIC), are available to evaluate the overall adequacy of linear regression models. In addition, visually assessing adequacy in models has become an essential part of any regression analysis. In this paper, we focus on a spatial consideration of the local DIC measure for model selection and goodness-of-fit evaluation. We use a partitioning of the DIC into the local DIC, leverage, and deviance residuals to assess local model fit and influence for both individual observations and groups of observations in a Bayesian framework. We use visualization of the local DIC and differences in local DIC between models to assist in model selection and to visualize the global and local impacts of adding covariates or model parameters. We demonstrate the utility of the local DIC in assessing model adequacy using HIV prevalence data from pregnant women in the Butare province of Rwanda during 1989-1993 using a range of linear model specifications, from global effects only to spatially varying coefficient models, and a set of covariates related to sexual behavior. Results of applying the diagnostic visualization approach include more refined model selection and greater understanding of the models as applied to the data.
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影响因子：
6.4
作者：
ALLEN, S;VANDEPERRE, P;HULLEY, S
通讯作者：
HULLEY, S
影响因子：
3.8
作者：
Boerma, JT;Urassa, M;Ng'weshemi, JZL
通讯作者：
Ng'weshemi, JZL
影响因子：
3.7
作者：
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通讯作者：
Banerjee, S
影响因子：
2.5
作者：
Gelman, Andrew;Pardoe, Lain
通讯作者：
Pardoe, Lain
影响因子：
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作者：
Haw, S;Higgins, K
通讯作者：
Higgins, K