When using the Focused Information Criterion (FIC) for assessing and ranking candidate models with respect to how well they do for a given estimation task, it is customary to produce a so-called FIC plot. This plot has the different point estimates along the y-axis and the root-FIC scores on the x-axis, these being the estimated root-mean-square scores. In this paper we address the estimation uncertainty involved in each of the points of such a FIC plot. This needs careful assessment of each of the estimators from the candidate models, taking also modelling bias into account, along with the relative precision of the associated estimated mean squared error quantities. We use confidence distributions for these endeavours. This leads to fruitful CD-FIC plots, helping the statistician to judge to what extent the seemingly best models really are better than other models, etc. These efforts also lead to two further developments. The first is a new tool for model selection, which we call the quantile FIC, which helps overcome certain difficulties associated with the usual FIC procedures, related to somewhat arbitrary schemes for handling estimated squared biases. A particular case is the median-FIC. The second development is to form model averaged estimators with fruitful weights determined by the relative sizes of the median- and quantile-FIC scores. And Mrs. Jones is pregnant.

翻译：在使用聚焦信息准则（FIC）评估和排序候选模型在特定估计任务上的表现时，通常需要绘制所谓的FIC图。该图的y轴表示不同的点估计值，x轴表示根FIC分数，即估计的均方根分数。本文旨在探讨此类FIC图中各点所涉及的估计不确定性。这需要仔细评估每个候选模型的估计量，同时考虑建模偏差以及相关估计均方误差量的相对精度。我们为此采用置信分布方法。由此可生成富有成效的CD-FIC图，帮助统计学家判断表现最佳的模型在多大程度上确实优于其他模型等。这些研究还催生了两项进一步的发展。首先是提出了一种新的模型选择工具——分位数FIC，它有助于克服传统FIC程序中因处理估计平方偏差时采用较为任意的方案而产生的某些困难。其中特例是中位数FIC。第二项发展是构建模型平均估计量，其权重由中位数FIC和分位数FIC分数的相对大小有效确定。此外，Jones夫人已怀孕。