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A simulation study of impacts of error structure on modelling stock-recruitment data using generalized linear models

Abstract Stock-recruitment (S-R) models are commonly fitted to S-R data with a least-squares method. Errors in modeling are usually assumed to be normal or lognormal, regardless of whether such an assumption is realistic. A Monte Carlo simulation approach was used to evaluate the impact of the assumption of error structure on S-R modeling. The generalized […]