New Measures of Skewness of a Probability Distribution  

作　　者：Ashok K. Singh[1] Laxmi P. Gewali[1] Jiwan Khatiwada[1] 

出　　处：《Open Journal of Statistics》2019年第5期601-621,共21页统计学期刊（英文）

摘　　要：Symmetry of the underlying probability density plays an important role in statistical inference, since the sampling distribution of the sample mean for a given sample size is more likely to be approximately normal for a symmetric distribution than for an asymmetric one. In this article, two new measures of skewness are proposed and the confidence intervals for true skewness are obtained via Monte Carlo simulation experiments. One advantage of the two proposed skewness measures over the standard measures of skewness is that the proposed measures of skewness take values inside the range (-1, +1).Symmetry of the underlying probability density plays an important role in statistical inference, since the sampling distribution of the sample mean for a given sample size is more likely to be approximately normal for a symmetric distribution than for an asymmetric one. In this article, two new measures of skewness are proposed and the confidence intervals for true skewness are obtained via Monte Carlo simulation experiments. One advantage of the two proposed skewness measures over the standard measures of skewness is that the proposed measures of skewness take values inside the range (-1, +1).

关 键 词：Sample MOMENTS QUANTILES Computational Geometry SYMMETRY Robust Measure Central Limit Theorem TRAPEZOID Rule 

分 类 号：O17[理学—数学]