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Standardizing compositional data for stock assessment

机译：标准化成分数据以进行库存评估

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Stock assessment models frequently integrate abundance index and compositional (e.g. age, length, sex) data. Abundance indices are generally estimated using index standardization models, which provide estimates of index standard errors while accounting for: (ⅰ) differences in sampling intensity spatially or over time; (ⅱ) non-independence of available data; and (ⅲ) the effect of covariates. However, compositional data are not generally processed using a standardization model, so effective sample size is not routinely estimated and these three issues are unresolved. I therefore propose a computationally simple "normal approximation" method for standardizing compositional data and compare this with design-based and Dirichlet-multinomial (D-M) methods for analysing compositional data. Using simulated data from a population with multiple spatial strata, heterogeneity within strata, differences in sampling intensity, and additional overdispersion, I show that the normal-approximation method provided unbiased estimates of abundance-at-age and estimates of effective sample size that are consistent with the imprecision of these estimates. A conventional design-based method also produced unbiased age compositions estimates but no estimate of effective sample size. The D-M failed to account for known differences in sampling intensity (the proportion of catch for each fishing trip that is sampled for age) and hence provides biased estimates when sampling intensity is correlated with variation in abundance-at-age data. I end by discussing uses for "composition-standardization models" and propose that future research develop methods to impute compositional data in strata with missing data.
机译：库存评估模型通常会整合丰度指数和成分（例如年龄，身长，性别）数据。丰度指标通常使用指标标准化模型进行估算，该模型可提供指标标准误差的估算，同时考虑：（ⅰ）空间或时间上的采样强度差异； （ⅱ）非独立可用数据； （ⅲ）协变量的影响。但是，一般不会使用标准化模型来处理成分数据，因此无法常规估算有效样本量，并且这三个问题都无法解决。因此，我提出了一种计算简单的“正态近似”方法，用于标准化成分数据，并将其与用于分析成分数据的基于设计的方法和Dirichlet多项式（D-M）方法进行比较。使用来自具有多个空间阶层，不同阶层内的异质性，采样强度的差异以及其他过度分散的人口的模拟数据，我表明正态近似方法提供了一致的年龄估计和有效样本大小的估计，这些估计是一致的这些估算值不精确。传统的基于设计的方法还产生了无偏的年龄成分估计值，但没有有效样本大小的估计值。 D-M无法解释采样强度的已知差异（按年龄采样的每次捕鱼行程的渔获量比例），因此当采样强度与年龄数据的变化相关时，提供了有偏差的估计。最后，我讨论了“组合物标准化模型”的用法，并建议未来的研究开发一些方法来在缺少数据的情况下将地层数据归入地层。

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