标题： 使用估计函数的MCMC开放式夹心调整 显示英文标题

标题： The open-faced sandwich adjustment for MCMC using estimating functions

摘要： 在处理似然函数时，经常会遇到问题的情况。 这可能有几个原因——也许似然是计算上过于昂贵，也许它缺乏某种稳健性特性，或者也许对于所考虑的模型来说，它根本未知。 在这些情况下，通常可以指定参数和数据的替代函数，这些函数可以被最大化以获得渐近正态的估计值。 然而，如果对应用贝叶斯技术感兴趣的话，这些情况会带来明显的问题。 在这里，我们描述了开放三明治调整，这是一种将广泛的非似然目标函数纳入类似贝叶斯模型的方法，通过MCMC获得渐近有效的参数估计和推断。 两个模拟示例显示，该方法提供了准确的频率不确定性估计。 开放三明治调整被应用于一个泊松时空模型，以分析来自公民科学倡议eBird的鸟类学数据集。 显示英文摘要

摘要： The situation frequently arises where working with the likelihood function is problematic. This can happen for several reasons---perhaps the likelihood is prohibitively computationally expensive, perhaps it lacks some robustness property, or perhaps it is simply not known for the model under consideration. In these cases, it is often possible to specify alternative functions of the parameters and the data that can be maximized to obtain asymptotically normal estimates. However, these scenarios present obvious problems if one is interested in applying Bayesian techniques. Here we describe open-faced sandwich adjustment, a way to incorporate a wide class of non-likelihood objective functions within Bayesian-like models to obtain asymptotically valid parameter estimates and inference via MCMC. Two simulation examples show that the method provides accurate frequentist uncertainty estimates. The open-faced sandwich adjustment is applied to a Poisson spatio-temporal model to analyze an ornithology dataset from the citizen science initiative eBird.