A Hybrid Pairwise Likelihood Method
一种混合成对似然方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究一般多元分布的复合似然；序数响应与边际分布措辞不足以明确归入经济离散选择或分布效应。
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一种混合成对似然方法
- 研究问题
- 当完整多元似然难以处理时，如何改进成对似然对边际参数的估计，并降低二元分布设定错误对这些估计的影响？
- 方法与思路
- 作者用边际得分函数的最优线性组合替代边际参数的成对似然得分方程，并讨论与交替逻辑斯蒂回归的关系。
- 材料陈述的贡献
- 摘要称边际分布正确时，该方法对二元分布误设稳健；并在特定序数模型比较和负二项模拟中报告相对改进。
- 阅读关注点
- 应核对最优组合所依赖的协方差信息、稳健性只覆盖哪些参数，以及理论比较、模拟场景和两个应用的限制。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；稳健性依赖一元边际正确设定，所述优势不代表所有场景，不能推断刊本变化。
原文摘要
A modification to the pairwise likelihood method is proposed, which aims to improve the estimation of the marginal distribution parameters. This is achieved by replacing the pairwise likelihood score equations, for estimating such parameters, by the optimal linear combinations of the marginal score functions. A further advantage of the proposed estimator of marginal parameters, over pairwise likelihood, is that it is robust to misspecification of the bivariate distributions as long as the univariate marginal distributions are correctly specified. While alternating logistic regression can be seen as a special case of the proposed method, it is shown that an existing generalization of alternating logistic regression applicable to ordinal data is not the same as and is inferior to the proposed method because it replaces certain conditional densities by pseudodensities that assume working independence. The fitting of the multivariate negative binomial distribution is another scenario involving intractable likelihood that calls for the use of pairwise likelihood methods, and the superiority of the modified method is demonstrated in a simulation study. Two examples, based on the analyses of salamander mating and patient-controlled analgesia data, demonstrate the usefulness of the proposed method. The possibility of combining optimally the pairwise, rather than marginal, scores is also considered and its difficulty and potential are discussed.