期刊名称：Pakistan Journal of Statistics and Operation Research
印刷版ISSN：2220-5810
出版年度：2015
卷号：11
期号：1
页码：91-102
DOI：10.18187/pjsor.v11i1.500
语种：English
出版社：College of Statistical and Actuarial Sciences
摘要：Tukey’s boxplot is very popular tool for detection of outliers. It reveals the location, spread and skewness of the data. It works nicely for detection of outliers when the data are symmetric. When the data are skewed it covers boundary away from the whisker on the compressed side while declares erroneous outliers on the extended side of the distribution. Hubert and Vandervieren (2008) made adjustment in Tukey’s technique to overcome this problem. However another problem arises that is the adjusted boxplot constructs the interval of critical values which even exceeds from the extremes of the data. In this situation adjusted boxplot is unable to detect outliers. This paper gives solution of this problem and proposed approach detects outliers properly. The validity of the technique has been checked by constructing fences around the true 95% values of different distributions. Simulation technique has been applied by drawing different sample size from chi square, beta and lognormal distributions. Fences constructed by the modified technique are close to the true 95% than adjusted boxplot which proves its superiority on the existing technique.
其他摘要：Tukey’s boxplot is very popular tool for detection of outliers. It reveals the location, spread and skewness of the data. It works nicely for detection of outliers when the data are symmetric. When the data are skewed it covers boundary away from the whisker on the compressed side while declares erroneous outliers on the extended side of the distribution. Hubert and Vandervieren (2008) made adjustment in Tukey’s technique to overcome this problem. However another problem arises that is the adjusted boxplot constructs the interval of critical values which even exceeds from the extremes of the data. In this situation adjusted boxplot is unable to detect outliers. This paper gives solution of this problem and proposed approach detects outliers properly. The validity of the technique has been checked by constructing fences around the true 95% values of different distributions. Simulation technique has been applied by drawing different sample size from chi square, beta and lognormal distributions. Fences constructed by the modified technique are close to the true 95% than adjusted boxplot which proves its superiority on the existing technique.
