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Robust parameter estimations using L-moments, TL-moments and the order statistics

机译:使用L矩,TL矩和阶数统计进行可靠的参数估计

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Application of the method of moments for the parametric distribution is common in the construction of a suitable parametric distribution. However, moment method of parameter estimation does not produce good results. An alternative approach when constructing an appropriate parametric distribution for the considered data file is to use the so-called order statistics. This paper deals with the use of order statistics as the methods of L-moments and TL-moments of parameter estimation. L-moments have some theoretical advantages over conventional moments. L-moments have been introduced as a robust alternative to classical moments of probability distributions. However, L-moments and their estimations lack some robust features that belong to the TL-moments. TL-moments represent an alternative robust version of L-moments, which are called trimmed L-moments. This paper deals with the use of L-moments and TL-moments in the construction of models of wage distribution. Three-parametric lognormal curves represent the basic theoretical distribution whose parameters were simultaneously estimated by three methods of point parameter estimation and accuracy of these methods was then evaluated. There are method of TL-moments, method of L-moments and maximum likelihood method in combination with Cohen's method. A total of 328 wage distribution has been the subject of research.
机译:在适当的参数分布的构造中,矩量法用于参数分布的应用是常见的。但是,参数估计的矩量法不能产生良好的结果。为所考虑的数据文件构造适当的参数分布时,另一种方法是使用所谓的顺序统计信息。本文将阶跃统计用作参数估计的L矩和TL矩的方法。 L矩相对于传统矩具有一些理论优势。 L矩已被引入作为经典概率分布矩的可靠替代。但是,L矩及其估计缺少一些属于TL矩的稳健特征。 TL力矩表示L力矩的替代健壮版本,称为修剪L力矩。本文探讨了在工资分配模型的构建中使用L-矩和TL-矩。三参数对数正态曲线表示基本的理论分布,其参数是通过点参数估计的三种方法同时估计的,然后评估了这些方法的准确性。结合Cohen方法,有TL矩法,L矩法和最大似然法。共研究了328份工资分配。

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