Bayesian Tobit quantile regression model for medical expenditure panel survey data
Bayesian Tobit quantile regression model for medical expenditure panel survey data
复制标题
医疗支出面板调查数据的贝叶斯托比特分位数回归模型
DOI：
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复制
发表时间：
2012
期刊：
影响因子：
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通讯作者：
H. Hong
中科院分区：
文献类型：
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作者：
Y. Yue;H. Hong
医疗保健的高支出是美国经济的重要组成部分，这使得医疗保健成本建模在广泛领域的决策过程中具有重要价值。在本文中，我们分析了医疗支出面板调查（MEPS）数据。Tobit回归模型在医疗费用预测中得到了广泛的应用。然而，它不再是足够的MEPS数据，因为：（i）支出的分布显示偏态，厚尾和异质性;（ii）大多数预测因子是分类的，包括二进制，名义和有序变量;（iii）有几个预测因子可能是非线性相关的响应。因此，我们提出了一个贝叶斯Tobit分位数回归模型来描述一个完整的分布视图上的医疗支出如何依赖于各种预测。具体来说，我们假设一个不对称的拉普拉斯误差分布，以适应分位数回归贝叶斯设置。然后，我们提出了一个修改后的组套索分类因子的选择，和一个平滑高斯先验建模的非线性效应。估计和他们的不确定性得到了一个有效的蒙特卡罗马尔可夫链抽样方法。通过对2007年MEPS数据的建模，证明了我们方法的有效性。
High expenditure on healthcare is an important segment of the U.S. economy, making healthcare cost modelling valuable in decision-making processes over a wide array of domains. In this paper, we analyze medical expenditure panel survey (MEPS) data. Tobit regression model has been popularly used for the medical expenditures. However, it is no longer sufficient for the MEPS data because: (i) the distribution of the expenditures shows skewness, heavy tails and heterogeneity; (ii) most predictors are categorical, including binary, nominal and ordinal variables; (iii) there are a few predictors which may be nonlinearly related to the response. We therefore propose a Bayesian Tobit quantile regression model to describe a complete distributional view on how the medical expenditures depend on the various predictors. Specifically, we assume an asymmetric Laplace error distribution to adapt the quantile regression to a Bayesian setting. Then, we propose a modified group Lasso for categorical factor selection, and a smoothing Gaussian prior for modelling the nonlinear effects. The estimates and their uncertainties are obtained using an efficient Monte Carlo Markov Chain sampling method. The effectiveness of our approach is demonstrated by modelling 2007 MEPS data.
通过回归进行平滑分位数比估计：估计吸烟引起的疾病的医疗支出。
DOI：
10.1093/biostatistics/kxi031
发表时间：
2005
期刊：
Biostatistics (Oxford, England)
影响因子：
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作者：
Dominici,Francesca;Zeger,ScottL
通讯作者：
Zeger,ScottL