Order-Restricted Bayesian Estimation of the Age Composition of a Population of Atlantic Cod
大西洋鳕鱼种群年龄组成的序约束贝叶斯估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究渔业种群年龄组成，既非经济分布效应，也未涉及所列宏观预测模型。
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大西洋鳕鱼种群年龄组成的序约束贝叶斯估计
- 研究问题
- 如何利用鱼体长度分层内及层间年龄组成的平滑性信息，改善调查数据对鳕鱼种群年龄比例及管理相关量的估计？
- 方法与思路
- 作者将单峰序约束纳入贝叶斯先验，同时允许峰值位置与单峰性本身存在不确定性，并用 Gibbs 抽样计算。
- 材料陈述的贡献
- 摘要称所分析数据中序约束带来较大精度收益，约半数样本可达到常规分析的相近精度，并改善渔获预测相关量估计。
- 阅读关注点
- 应核对两阶段分层抽样如何进入模型、序约束与先验的构造、精度比较标准，以及偏离单峰条件时的敏感性。
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原文摘要
The management of a commercial fishery often requires estimates of the age composition. These estimates are typically based on age and length data obtained from sampling the commercial landings from the fishery and the catches from a research vessel survey. We use data from annual research cruises of Georges Bank conducted by the Canadian Department of Fisheries and Oceans to show how inferences for Atlantic cod (Gadus morhua) can be improved. Traditionally, two-phase stratified sampling is used with fish length (or weight) as the stratification variable. Letting Pi . denote the proportion of fish belonging to length stratum i, and π ij denote the proportion of fish belonging to age class j in stratum i, we use Bayesian methods to estimate P.j = π i πij , the proportion of fish that are age j. Specifications of smoothness, expressed as unimodal order relations among the π ij (within and between the length strata), are incorporated into the prior distributions. Uncertainty about both the locations of the modes and the unimodality itself are included as part of the probabilistic specification. With computations facilitated by using the Gibbs sampler, we show that the smoothness conditions provide very large gains in precision. For the data analyzed in this article, one can obtain similar precision by using (a) a conventional analysis or (b) an analysis with order restrictions and a sample of half the size in (a). We also show that better estimates of the age composition provide improved estimates of the quantities used by fisheries managers to forecast the catches from cohorts of fish.