Our main contribution is to examine the reliability of confidence intervals using the SAM state-space fish stock assessment model used for the assessment of many stocks by the International Council for the Exploration of the Seas. We focus on frequentist statistical inferences and more specifically on inference conditioned on specific values of the state-space model random effects drawn from their process distribution. This is somewhat consistent with simulation self-test procedures that are commonly used to examine the reliability of state-space assessment model results. However, recent research has indicated that some estimation bias may be expected in the conditional setting. Hence, we also investigate recently proposed bias corrected confidence intervals appropriate for the conditional inference setting. The SAM simulation coverage probabilities of 95% confidence intervals for SSB and Fbar were usually slightly larger than 95%, but in a small number of years these coverage probabilities could be much smaller than 95%. The bias corrected confidence intervals were more reliable. When averaged over years, the SAM and bias corrected confidence interval coverage probabilities were similar for the Northeast Artic cod and saithe case studies, but the bias corrected confidence intervals performed much better overall for the haddock case study.

我们的主要贡献是使用 SAM 状态空间鱼类种群评估模型来检查置信区间的可靠性，该模型用于国际海洋勘探理事会评估许多种群。我们关注频率统计推断，更具体地说，关注以从过程分布中得出的状态空间模型随机效应的特定值为条件的推断。这与通常用于检查状态空间评估模型结果的可靠性的模拟自检程序有些一致。然而，最近的研究表明，在条件设置下可能会出现一些估计偏差。因此，我们还研究了最近提出的适合条件推理设置的偏差校正置信区间。SSB 和 Fbar 95% 置信区间的 SAM 模拟覆盖概率通常略大于 95%，但在少数年份中，这些覆盖概率可能远小于 95%。偏差校正置信区间更可靠。多年平均后，东北北极鳕鱼和上述案例研究的 SAM 和偏差校正置信区间覆盖概率相似，但黑线鳕案例研究的偏差校正置信区间总体表现要好得多。