Korean Journal of Veterinary Research 2007;47(2):259-263.
Bootstrap simulation for quantification of uncertainty in risk assessment
Ki-Yoon Chang1, Ki-Ok Hong1, Son-Il Pak2
1Animal Health Division, Livestock Bureau, Ministry of Agriculture & Forestry
2School of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University
The choice of input distribution in quantitative risk assessments modeling is of great importance to get unbiased overall estimates, although it is difficult to characterize them in situations where data available are too sparse or small. The present study is particularly concerned with accommodation of uncertainties commonly encountered in the practice of modeling. The authors applied parametric and non-parametric bootstrap simulation methods which consist of re-sampling with replacement, in together with the classical Student-t statistics based on the normal distribution. The implications of these methods were demonstrated through an empirical analysis of trade volume from the amount of chicken and pork meat imported to Korea during the period of 1998-2005. The results of bootstrap method were comparable to the classical techniques, indicating that bootstrap can be an alternative approach in a specific context of trade volume. We also illustrated on what extent the bias corrected and accelerated non-parametric bootstrap method produces different estimate of interest, as compared by non-parametric bootstrap method.
Key Words: bootstrap, risk analysis, simulation, uncertainty
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