Comparison of six statistical approaches in the selection of appropriate fish growth models

Lixin Zhu; Lifang Li; Zhenlin Liang

doi:10.1007/s00343-009-9236-6

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Comparison of six statistical approaches in the selection of appropriate fish growth models

Updated：2023-05-16

- Comparison of six statistical approaches in the selection of appropriate fish growth models
- Journal of Oceanology and Limnology Vol. 27, Issue 3, Pages: 457-467(2009)
- Affiliations：
  
  1. Marine College Shandong University at Weihai
  2. Fisheries College Ocean University of China
- Author bio：
- Funds：
- DOI：10.1007/s00343-009-9236-6
  CLC：
- Online First：07 November 2009，
  
  Published：2009
- Accepted：
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Zhu, L., Li, L. & Liang, Z. Comparison of six statistical approaches in the selection of appropriate fish growth models., Chin. J. Ocean. Limnol. 27, 457–467 (2009). https://doi.org/10.1007/s00343-009-9236-6
DOI：

Zhu, L., Li, L. & Liang, Z. Comparison of six statistical approaches in the selection of appropriate fish growth models., Chin. J. Ocean. Limnol. 27, 457–467 (2009). https://doi.org/10.1007/s00343-009-9236-6 DOI：

摘要

Abstract

The performance of six statistical approaches

which can be used for selection of the best model to describe the growth of individual fish

was analyzed using simulated and real length-at-age data. The six approaches include coefficient of determination (R 2)

adjusted coefficient of determination (adj.-R 2)

root mean squared error (RMSE)

Akaike’s information criterion (AIC)

bias correction of AIC (AIC c ) and Bayesian information criterion (BIC). The simulation data were generated by five growth models with different numbers of parameters. Four sets of real data were taken from the literature. The parameters in each of the five growth models were estimated using the maximum likelihood method under the assumption of the additive error structure for the data. The best supported model by the data was identified using each of the six approaches. The results show that R 2 and RMSE have the same properties and perform worst. The sample size has an effect on the performance of adj.-R 2

AIC

AIC c and BIC. Adj.-R 2 does better in small samples than in large samples. AIC is not suitable to use in small samples and tends to select more complex model when the sample size becomes large. AIC c and BIC have best performance in small and large sample cases

respectively. Use of AIC c or BIC is recommended for selection of fish growth model according to the size of the length-at-age data.

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Keywords

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