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A comparison of parametric, semi-parametric, and non-parametric approaches to selectivity in age-structured assessment models

机译：参数，半参数和非参数方法在年龄结构评估模型中的选择性比较

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Integrated assessment models frequently track population abundance at age, and hence account for fishery removals using a function representing fishery selectivity at age. However, fishery selectivity may have an unusual shape that does not match any parametric function. For this reason, previous research has developed flexible 'non-parametric' models for selectivity that specify a penalty on changes in selectivity as a function of age. In this study, we describe an alternative 'semi-parametric' approach to selectivity, which specifies a penalty on differences between estimated selectivity at age and a pre-specified parametric model whose parameters are freely estimated, while also using cross-validation to select the magnitude of penalty in both semi- and non-parametric models. We then compare parametric, semi-parametric, and non-parametric models using simulated data and evaluate the bias and precision of estimated depletion and fishing intensity. Results show that semi- and non-parametric models result in little decrease in precision relative to the parametric model when the parametric model matches the true data-generating process, but that the semi- and non-parametric models have less bias and greater precision when the parametric function is misspecified. As expected, the semi-parametric model reverts to its pre-specified parametric form when age-composition sample size is low but performs similarly to the non-parametric model when sample size is high. Overall, results indicate few disadvantages to using the non-parametric model given the range of simulation scenarios explored here, and that the semi-parametric model provides a selectivity specification that is intermediate between parametric and non-parametric forms
机译：综合评估模型经常跟踪年龄段的人口丰度，因此使用代表年龄段渔业选择性的函数来说明渔业捕捞量。但是，渔业选择性可能具有不匹配任何参数函数的异常形状。因此，先前的研究开发了灵活的“非参数”选择性模型，该模型规定了随年龄变化的选择性变化的惩罚。在这项研究中，我们描述了选择性的另一种“半参数”方法，该方法规定了对年龄估算的选择性与参数可自由估算的预先指定参数模型之间差异的惩罚，同时还使用交叉验证来选择半参数模型和非参数模型的惩罚幅度。然后，我们使用模拟数据比较参数模型，半参数模型和非参数模型，并评估估计的消耗量和捕捞强度的偏差和精度。结果表明，当参数模型与真实数据生成过程匹配时，半参数模型和非参数模型的精度相对于参数模型几乎没有下降，但是当参数模型与真实数据生成过程匹配时，半参数模型和非参数模型的偏差较小且精度较高参数函数指定不正确。正如预期的那样，当年龄组成样本量较小时，半参数模型将恢复为预先指定的参数形式，但是当样本量较大时，半参数模型的性能与非参数模型相似。总体而言，结果表明在给定的模拟场景范围内，使用非参数模型几乎没有弊端，并且半参数模型提供的选择规范介于参数和非参数形式之间

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