Modeling of extreme values like annual maxima’s is important in many applications. Pearson Type-3 (PE3) distribution is an important probability distribution, widely used for modeling of extreme values with a variety of estimation methods. The focus of this study is to assess the effects of three methods of estimation of parameters for PE3 distribution namely L-moments (LM), maximum likelihood estimation (MLE) and maximum product of spacing (MPS). Assessment is based on a two-step approach. The first step uses simulation experiments while the second is based on empirical analyses, by varying size and shape characteristics of the sample. The study concluded that the estimates using LM method have low bias in case of small sample and when data exhibits small to moderate skewness and kurtosis. MPS is a reasonable alternative and provides efficient estimates, especially when the data shows large skewness and kurtosis with small to moderate size of sample. MLE method is useful in case of very large sample size with low values of shape characteristics of data. The results of this study provide useful guidelines for fitting PE3 distribution, especially to extreme values.

在许多应用中，像年度最大值这样的极值建模非常重要。Pearson Type-3（PE3）分布是一种重要的概率分布，已广泛用于通过各种估算方法对极值进行建模。这项研究的重点是评估PE3分布参数的三种估算方法的效果，即L矩（LM），最大似然估计（MLE）和最大间距乘积（MPS）。评估基于两步法。第一步使用模拟实验，而第二步基于经验分析，通过改变样品的大小和形状特征。研究得出的结论是，在样本量较小且数据显示偏小和中等偏度和峰度的情况下，使用LM方法进行的估计具有较低的偏倚。MPS是一种合理的选择，可以提供有效的估计，尤其是当数据显示较大的偏度和峰度且样本量较小到中等时。如果样本量非常大而数据的形状特征值较低，则MLE方法很有用。这项研究的结果为拟合PE3的分布提供了有用的指导，尤其是达到极值时。