Estimating Sample Skewness from Sample Data Summaries and Associated Evaluation of Normality

IF 0.8 Q3 STATISTICS & PROBABILITY

Mathematical Methods of Statistics Pub Date : 2023-12-23 DOI:10.3103/s106653072304004x

Narayanaswamy Balakrishnan, Jan Rychtář, Dewey Taylor

引用次数: 0

Abstract

We propose a method to estimate a sample skewness from the given summary statistics and give explicit formulas for the most common scenarios. We show that our method provides a nearly unbiased estimator for the non-parametric skewness measure. We empirically evaluate the performance on real-life data sets of COVID-19 vaccination status. We also demonstrate how the method can be applied to detect the skewness of the underlying distribution.

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从样本数据摘要估算样本偏度及相关的正态性评估

摘要我们提出了一种根据给定的汇总统计量估计样本偏斜度的方法，并给出了最常见情况的明确公式。我们的研究表明，我们的方法为非参数偏度测量提供了一个几乎无偏的估计值。我们在 COVID-19 疫苗接种情况的真实数据集上对该方法的性能进行了实证评估。我们还演示了如何应用该方法来检测底层分布的偏度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mathematical Methods of Statistics STATISTICS & PROBABILITY-

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Mathematical Methods of Statistics is an is an international peer reviewed journal dedicated to the mathematical foundations of statistical theory. It primarily publishes research papers with complete proofs and, occasionally, review papers on particular problems of statistics. Papers dealing with applications of statistics are also published if they contain new theoretical developments to the underlying statistical methods. The journal provides an outlet for research in advanced statistical methodology and for studies where such methodology is effectively used or which stimulate its further development.