Abstract
Extreme value type 1 distribution parameters and quantiles were estimated by methods of moments, maximum likelihood estimation, Probability weighted moments, entropy, mixed moments, least squares and incomplete means for Monte Carlo samples generated from two Sampling cases: purely random process and serially correlated process. The performance of these estimators was statistically evaluated. The methods of maximum likelihood estimation and entropy provided most efficient quantile estimates. The methods of moments and probability weighted moments were comparable in efficiency of estimating the quantiles for small samples. The methods of mixed moments and incomplete means resulted in poor estimation of parameters and the quantiles.
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