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首页> 外文期刊>Statistica neerlandica >Bayesian and Frequentist Inference for Ecological Inference: The R×C Case
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Bayesian and Frequentist Inference for Ecological Inference: The R×C Case

机译：贝叶斯和惯常论的生态推理： R × C 案例

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摘要

In this paper we propose Bayesian and frequentist approaches to ecological inference, based on R×C contingency tables, including a covariate. The proposed Bayesian model extends the binomial-beta hierarchical model developed by King, Rosen and Tanner (1999) from the 2×2 case to the R×C case. As in the 2×2 case, the inferential procedure employs Markov chain Monte Carlo (MCMC) methods. As such, the resulting MCMC analysis is rich but computationally intensive. The frequentist approach, based on first moments rather than on the entire likelihood, provides quick inference via nonlinear least-squares, while retaining good frequentist properties. The two approaches are illustrated with simulated data, as well as with real data on voting patterns in Weimar Germany. In the final section of the paper we provide an overview of a range of alternative inferential approaches which trade-off computational intensity for statistical efficiency.
机译：在本文中，我们基于R×C列联表（包括协变量）提出了贝叶斯和常人的生态推理方法。提出的贝叶斯模型将King，Rosen和Tanner（1999）开发的二项式-β层次模型从2×2情况扩展到R×C情况。与2×2情况一样，推导过程采用马尔可夫链蒙特卡罗（MCMC）方法。因此，所得的MCMC分析内容丰富，但计算量大。基于第一时刻而不是整个可能性的常客方法通过非线性最小二乘提供快速推断，同时保留了良好的常客属性。两种方法均通过仿真数据以及德国魏玛投票模式的真实数据进行了说明。在本文的最后部分，我们提供了一系列可供选择的推理方法的概述，这些方法在计算强度上进行权衡以提高统计效率。

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