ESTIMATING DISTRIBUTION PARAMETERS USING OPTIMIZATION TECHNIQUES

An improved parameter estimation procedure has been developed by using optimization techniques and applied to estimate the parameters of the log-Pearson type 3 (LP3) distribution. As a result, an improved estim ation method was found. The new methods estimates the mean and the sta ndard deviation of the log-transformed data by the method of moments a nd estimates the coefficient of skewness by minimizing both the relati ve root average square error (RRASE) and the relative average bias (RA B). Monte Carlo simulation was conducted for four selected LP3 populat ions. As compared with the method of moments, larger reductions in sta ndard root mean square error (SRMSE) and standard bias (SBIAS) for qua ntile prediction can be achieved by the new method for small sample si zes and large return periods of quantiles. In addition, the new method can always fit the observed data better than the method of moments.