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- W2389969266 abstract "Growth models are important components of population biology study and are generally essential to adequately assess the impact of fishery.Given a specific functional form,the appropriate estimation of growth parameters depends on the error structure assumed for the data.For example,if variability in size is constant as a function of age,an additive error structure is suitable.However,if the variability in size increases with age,a multiplicative error or variance modeling is appropriate.Variance heterogeneity will typically not in?uence the parameter estimates significantly,but if ignored it may result in severely misleading the standard error and predic-tion intervals.The four parameters model formulated by Schnute contains a number of specific growth models that can be used to explain the pattern of growth in small yellow croaker(Larimichthys polyactis).We used data trans-formation and variance modeling to investigate the effect of assuming a different error structure in the model.We used data from stow net surveys conducted between May–September in 2007–2008 and from bottom trawls con-ducted in the northern region of the East China Sea between October–April in 2007–2009.We used the likelihood ratio test(c2 distribution) and Akaike's Information Criterion to quantitatively compare the fit of nested submodels.Error structure had a significant effect on the fitted models.The estimated parameter values for the lognormal er-ror,power variance,and exponential variance structure models were similar.Furthermore,relatively small standard errors and narrow confidence intervals suggest that the integration of variance structure in the growth models is more accurate and robust than in the additive models.The log-transformation models and variance structure mod-els fit the data better than the additive models.The funneling observed in the plots of deviance residuals against age for the additive models was reduced in the corresponding plots for the lognormal error and variance structure models.The power variance and exponential variance models yielded significantly different estimates than the additive models(c2 test,P0.001) and had lower AIC values.Together,our results suggest the integration of variance structure to estimate growth improves goodness of fit and predictive power." @default.
- W2389969266 created "2016-06-24" @default.
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- W2389969266 date "2012-01-01" @default.
- W2389969266 modified "2023-09-27" @default.
- W2389969266 title "Modeling variance heterogeneity in growth: An example for small yellow croaker,Larimichthys polyactis in the northern East China Sea" @default.
- W2389969266 hasPublicationYear "2012" @default.
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