BAYESIAN-ANALYSIS OF ERRORS-IN-VARIABLES REGRESSION-MODELS
BAYESIAN-ANALYSIS OF ERRORS-IN-VARIABLES REGRESSION-MODELS
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DOI：
10.2307/2533007
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发表时间：
1995-09-01
期刊：
影响因子：
1.9
通讯作者：
STEPHENS, DA
中科院分区：
文献类型：
--
作者：
DELLAPORTAS, P;STEPHENS, DA
在许多实际实验问题中使用变量误差模型是合适的。然而，基于此类模型的推理绝不是简单的。在之前的分析中，为了缓解这种棘手问题而做出了简化的假设，但这种性质的假设是不幸且具有限制性的。在本文中，我们在贝叶斯公式下全面分析变量误差模型。为了计算必要的后验分布，我们利用各种计算技术。考虑两个特定的非线性变量误差回归示例；第一个是重新分析的伯克森型模型，第二个是经典的变量错误模型。我们的分析与文献中其他地方提出的分析进行了比较和对比。
Use of errors-in-variables models is appropriate in many practical experimental problems. However, inference based on such models is by no means straightforward. In previous analyses, simplifying assumptions have been made in order to ease this intractability, but assumptions of this nature are unfortunate and restrictive. In this paper, we analyse errors-in-variables models in full generality under a Bayesian formulation. In order to compute the necessary posterior distributions, we utilize various computational techniques. Two specific non-linear errors-in-variables regression examples are considered; the first is a re-analysed Berkson-type model, and the second is a classical errors-in-variables model. Our analyses are compared and contrasted with those presented elsewhere in the literature.