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Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives
The Journal of the Royal Statistical Society: Series C (Applied Statistics) ( IF 1.6 ) Pub Date : 2020-01-07 , DOI: 10.1111/rssc.12393
Vincent Chin 1, 2 , David Gunawan 2, 3 , Denzil G. Fiebig 1, 2 , Robert Kohn 1, 2 , Scott A. Sisson 1, 2
Affiliation  

The paper considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high dimensional correlation matrix and improving the overall efficiency of the data augmentation approach. We reparameterize the correlation matrix in a principled way and then carry out efficient Bayesian inference by using Hamiltonian Monte Carlo sampling. We also propose a novel antithetic variable method to generate samples from the posterior distribution of the random effects and regression coefficients, resulting in significant gains in efficiency. We apply the methodology by analysing stated preference data obtained from Australian general practitioners evaluating alternative contraceptive products. Our analysis suggests that the joint probability of discussing combinations of contraceptive products with a patient shows medical practice variation among the general practitioners, which indicates some resistance even to discuss these products, let alone to recommend them.

中文翻译:

具有面板数据的多变量概率模型的有效数据增强:在全科医生避孕方法决策中的应用

本文考虑了在面板数据集中估算多元概率模型的问题,重点是对高维相关矩阵进行采样并提高数据增强方法的整体效率。我们以有原则的方式对相关矩阵进行重新参数化,然后使用汉密尔顿蒙特卡洛采样进行有效的贝叶斯推断。我们还提出了一种新颖的对偶变量方法,可从随机效应和回归系数的后验分布中生成样本,从而显着提高效率。我们通过分析从评估替代避孕产品的澳大利亚全科医生获得的指定偏好数据来应用该方法。
更新日期:2020-04-23
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