Identifiability, improper priors, and Gibbs sampling for generalized linear models
Identifiability, improper priors, and Gibbs sampling for generalized linear models
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DOI：
10.2307/2669699
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发表时间：
1999-03-01
影响因子：
3.7
通讯作者：
Sahu, K
中科院分区：
文献类型：
--
作者：
Gelfand, AE;Sahu, K
马尔可夫链蒙特卡罗算法广泛应用于广义线性模型的拟合。这种模型拟合在某种程度上是一种艺术形式，需要适当的技巧和调整来获得人们可以有信心的结果。由此产生了广泛的实际问题。这里的重点是参数可识别性和后验性。特别是，我们澄清了通常glm的不可识别性，并讨论了其对基于模拟的模型拟合的影响。由于通常某些部分的先验规范是模糊的，我们考虑所得后验是否适当，为glm提供相当一般和容易检查的结果。我们还表明，如果吉布斯采样器使用不适当的后验运行，则可能使用输出来获得对某些模型未知数有意义的推断。
Markov chain Monte Carlo algorithms are widely used in the fitting of generalized linear models (GLMs). Such model fitting is somewhat of an art form, requiring suitable trickery and tuning to obtain results in which one can have confidence. A wide range of practical issues arise. The focus here is on parameter identifiability and posterior propriety. In particular, we clarify that nonidentifiability arises for usual GLMs and discuss its implications for simulation-based model fitting. Because often some part of the prior specification is vague, we consider whether the resulting posterior is proper, providing rather general and easily checked results for GLMs. We also show that if a Gibbs sampler is run with an improper posterior, then it may be possible to use the output to obtain meaningful inference for certain model unknowns.