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将元分析结果用于贝叶斯更新：空单元格问题AI 解读

Bayesian estimation incorporating prior information has been a popular approach to gaining estimation efficiency. Although prior information can take a variety of forms, generalizations derived from meta-analyses have been suggested as being useful. This article shows that these priors can be problematic in light of the many empty cells observed in meta-analysis designs. Design reduction, which gives rise to an unbiased prior, is found to be the preferred solution.

在线性回归模型中结合相关研究与稀疏数据AI 解读

Meta-analysis has become a popular approach for studying systematic variation in parameter estimates across studies. this article discusses the use of meta-analysis results as prior information in a new study. Although hierarchical prior distributions in a traditional Bayesian framework are characterized by complete exchangeability, meta-analysis priors explicitly incorporate heterogeneity in prior vectors. This article discusses the nature of the meta-analysis priors, their…

异质条件 Logit 选择模型AI 解读

Aggregate forecasts using McFadden's conditional logit model of discrete choice harbor the unrealistic implicit assumption of a random-utility distribution that is homogeneous across both alternatives and individuals. This article relaxes that assumption. A choice model is developed that describes the random-utility component as a probability-mixture model. Some numerical results illustrate that the derived model is not constrained by the…