Validation of models with proportional bias
Validación de modelos con sesgo proporcional
Show authors biography
Objective. This paper presents extensions to Freese’s statistical method for model-validation when proportional bias (PB) is present in the predictions. The method is illustrated with data from a model that simulates grassland growth. Materials and methods. The extensions to validate models with PB were: the maximum anticipated error for the original proposal, hypothesis testing, and the maximum anticipated error for the alternative proposal, and the confidence interval for a quantile of error distribution. Results. The tested model had PB, which once removed, and with a confidence level of 95%, the magnitude of error does not surpass 1225.564 kg ha-1. Therefore, the validated model can be used to predict grassland growth. However, it would require a fit of its structure based on the presence of PB. Conclusions. The extensions presented to validate models with PB are applied without modification in the model structure. Once PB is corrected, the confidence interval for the quantile 1-α of the error distribution enables a higher bound for the magnitude of the prediction error and it can be used to evaluate the evolution of the model for a system prediction.
Article visits 1248 | PDF visits
Downloads
- Tedeschi LO. Assessment of the adequacy of mathematical models. Agr Syst 2006; 89(2-39):225–247.
- Medina-Peralta S, Vargas-Villamil L, Navarro-Alberto J, Canul-Pech C, Peraza-Romero S. Comparación de medidas de desviación para validar modelos sin sesgo, sesgo constante o proporcional. Univ Cienc. 2010; 26(3):255-263.
- Freese F. Testing accuracy. For Sci 1960; 6:139-45.
- Medina PS, Vargas-Villamil L, Navarro AJ, Avendaño L, Colorado L, Arjona-Suarez E, Mendoza-Martínez G. Validación de modelos con sesgo constante: un enfoque aplicado. Rev MVZ Córdoba 2014; 19(2):4099-08.
- Rennie JC, Wiant HV. Modification of Freese’s chi-square test of accuracy. USDI Bureau of Land Management, Denver Colorado. Resource Inventory 1978; 14:1-3.
- Reynolds MR. Estimating the error in model predictions. For Sci 1984; 30(2):454-69.
- Barrales VL, Peña RI, Fernández RB. Model validation: an applied approach. Agric Tech 2004; 64:66-73.
- Medina PS. Validación de modelos mecanísticos basada en la prueba ji-cuadrada de Freese, su modificación y extensión. [Master’s thesis]. Montecillo, México: Colegio de Postgraduados; 2006.
- Bickel PJ, Doksum KA. Mathematical Statistic: basic ideas and selected topics Vol I. Second edition. New Jersey: Pearson Prentice Hall; 2007.
- McCarthy MA, Possingham HP, Day JR, Tyre AJ. Testing the accuracy of population viability analysis. Conserv Biol 2001; 15:1030-38.
Information
