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Sampling schemes for generalized linear Dirichlet process random effects models

机译：广义线性Dirichlet过程随机效应模型的采样方案

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摘要

We evaluate MCMC sampling schemes for a variety of link functions in generalized linear models with Dirichlet process random effects. First, we find that there is a large amount of variability in the performance of MCMC algorithms, with the slice sampler typically being less desirable than either a Kolmogorov-Smir-nov mixture representation or a Metropolis-Hastings algorithm. Second, in fitting the Dirichlet process, dealing with the precision parameter has troubled model specifications in the past. Here we find that incorporating this parameter into the MCMC sampling scheme is not only computationally feasible, but also results in a more robust set of estimates, in that they are marginalized-over rather than conditioned-upon. Applications are provided with social science problems in areas where the data can be difficult to model, and we find that the nonparametric nature of the Dirichlet process priors for the random effects leads to improved analyses with more reasonable inferences.
机译：我们评估具有Dirichlet过程随机效应的广义线性模型中各种链接函数的MCMC采样方案。首先，我们发现MCMC算法的性能存在很大的可变性，通常切片采样器不如Kolmogorov-Smir-nov混合表示或Metropolis-Hastings算法可取。其次，过去在拟合Dirichlet过程中，处理精度参数困扰着模型规格。在这里，我们发现将这个参数合并到MCMC采样方案中不仅在计算上可行，而且还导致更可靠的估计集，因为它们被边缘化而不是条件化。在数据难以建模的领域，应用程序存在社会科学问题，并且我们发现随机效应的Dirichlet过程先验的非参数性质可以通过更合理的推断来改进分析。

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