On Bootstrap Tests of Symmetry About an Unknown Median

Journal of Data Science

article

Tian Zheng¹  Joseph L. Gastwirth² 

[1] Columbia University;George Washington University

关键词: Parametric bootstrap;    resampling;    testing symmetry about an unknown center;   

DOI  :  10.6339/JDS.2010.08(3).614

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

It is important to examine the symmetry of an underlying distribution before applying some statistical procedures to a data set. For example, in the Zuni School District case, a formula originally developed by the Department of Education trimmed 5% of the data symmetrically from each end. The validity of this procedure was questioned at the hearing by Chief Justice Roberts. Most tests of symmetry (even nonparametric ones) are not distribution free in finite sample sizes. Hence, using asymptotic distribution may not yield an accurate type I error rate or/and loss of power in small samples. Bootstrap resampling from a symmetric empirical distribution function fitted to the data is proposed to improve the accuracy of the calculated p-value of several tests of symmetry. The results show that the bootstrap method is superior to previously used approaches relying on the asymptotic distribution of the tests that assumed the data come from a normal distribution. Incorporating the bootstrap estimate in a recently proposed test due to Miao, Gel and Gastwirth (2006) preserved its level and shows it has reasonable power properties on the family of distribution evaluated.

【 授权许可】

CC BY