Catch-effort maximum likelihood estimation of important population parameters
Catch-effort maximum likelihood estimation of important population parameters
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DOI：
10.1139/cjfas-54-4-890
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发表时间：
1997-04-01
影响因子：
2.4
通讯作者：
Pollock, KH
中科院分区：
文献类型：
--
作者：
Gould, WR;Pollock, KH
线性回归模型的相对容易理解解释了这种技术在估计种群规模与渔获量数据的流行。然而，回归模型的开发和使用需要假设和近似值，这些假设和近似值可能无法准确反映现实。我们提出了必要的模型开发的最大似然估计的参数从捕捞努力的数据，使用程序SURVIV，主要目的是目前生物学家与车辆产生最大似然估计，而不是使用传统的回归技术。回归方法和最大似然估计之间的差异将以商业渔业渔获量数据为例并通过模拟加以说明。我们的研究结果表明，最大似然估计始终提供更少的偏见和更精确的估计比回归方法，并允许更大的模型灵活性，在许多情况下必要的。我们建议在未来的渔获量研究中使用最大似然估计。
The relative ease with which linear regression models are understood explains the popularity of such techniques in estimating population size with catch-effort data. However, the development and use of the regression models require assumptions and approximations that may not accurately reflect reality. We present the model development necessary for maximum likelihood estimation of parameters from catch-effort data using the program SURVIV, the primary intent being to present biologists with a vehicle for producing maximum likelihood estimates in lieu of using the traditional regression techniques. The differences between the regression approaches and maximum likelihood estimation will be illustrated with an example of commercial fishery catch-effort data and through simulation. Our results indicate that maximum likelihood estimation consistently provides less biased and more precise estimates than the regression methods and allows for greater model flexibility necessary in many circumstances. We recommend the use of maximum likelihood estimation in future catch-effort studies.