A bootstrap method for estimating bias and variance in statistical fisheries modelling frameworks using highly disparate datasets

Abstract

Statistical models of marine ecosystems use a variety of data sources to estimate parameters using composite or weighted likelihood functions with associated weighting issues and questions on how to obtain variance estimates. Regardless of the method used to obtain point estimates, a method is required for variance estimation. A bootstrap technique is introduced for the evaluation of uncertainty in such models, taking into account inherent spatial and temporal correlations in the datasets, which are commonly transferred as assumptions from a likelihood estimation procedure into Hessian-based variance estimation procedures. The technique is demonstrated on a real dataset and the effects of the number of bootstrap samples on estimation bias and variance estimates are studied. Although the modelling framework and bootstrap method can be applied to multispecies and multiarea models, for clarity the case study described is of a single-species and single-area model.

Keywords:

• bootstrapping
• correlated data
• fish population dynamics
• non-linear models