The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.
The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.
复制标题
使用简单的重新参数化来提高多级模型的马尔可夫链蒙特卡罗估计的效率，并应用于离散时间生存模型。
DOI：
10.1111/j.1467-985x.2009.00586.x
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发表时间：
2009-06
影响因子：
2
通讯作者：
Green, Martin J.
中科院分区：
文献类型：
--
作者：
Browne, William J.;Steele, Fiona;Golalizadeh, Mousa;Green, Martin J.
关键词：
我们考虑应用马尔可夫链蒙特卡罗（MCMC）估计方法的随机效应模型，特别是家庭的离散时间生存模型。生存模型可以在医学和社会科学的许多情况下使用，我们通过两个不同的实质性领域和数据结构的例子来说明它们的使用。多水平离散时间生存分析涉及扩展数据集，以便模型可以被转换为标准的多水平二进制响应模型。对于这样的模型，它已被证明，MCMC方法在减少估计偏差方面具有优势。然而，数据扩展导致非常大的数据集，对于这些数据集，MCMC估计通常很慢，并且可能产生表现出不良混合的链。任何改进混合的方法都将加快方法的速度，并提高对所产生的估计的信心。MCMC方法学文献充满了替代算法，旨在提高混合链，我们描述了三个reparameterization技术，很容易在现有的软件中实现。我们认为两个例子多层次的生存分析：乳腺炎的发病率在奶牛和避孕药的使用动态在印度尼西亚。对于每一个应用程序，我们展示了重新参数化技术可以使用，并评估其性能。
We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.