Citation
Wong, Hui Shein
(2016)
Modified sequential fences for identifying univariate outliers.
Masters thesis, Universiti Putra Malaysia.
Abstract
The existence of outliers in data set can bring some impacts on statistical data analysis
and affect decision making. Thus, it is vital for researcher to identify the outliers.
Sequential fences is a graphical method which was proposed by Schewertman and de
Silva (2007). Besides its simplicity, this method is also effective in detecting multiple
outliers while maintaining the approximate specific outside rate at each stage as the
series on number of outlier fences. This research focuses on the modification of
sequential fences to improve its efficiency.
Sequential fences method is modified by replacing interquartile range with various
robust scales such as semi-interquartile range, , median absolute deviation
( ) and Gini’s mean difference ( ) in order to improve outlier detection in
symmetric distribution. Ultimately, the utilisation of in sequential fences seems
to demonstrate a comparable accuracy in detecting the contaminated data. We have
shown that GSF approach effectively reduce the masking and swamping problems in
identifying the outliers.
Furthermore, a new approach is proposed by considering the skewness of underlying
distribution to increase efficiency of sequential fences in skewed distribution.
Conclusively, based on the numerical examples and simulation study, newly proposed
method has been adjusted according to the skewness of the underlying distribution of
data. The results show that the new approach performed better in reducing swamping
effect which is misclassifying non-contaminated observation as outlier in asymmetric
distribution. Moreover, we proposed a new method with modified algorithm and methodology
namely bootstrap sequential fences. The proposed method involves initial screening of
data and bootstrap technique to improve the performance of sequential fences. The
modified sequential fences method is found can accurately detect the outliers in
positively skewed distribution. In addition, this proposed method also estimates
trimmed mean and trimmed standard deviation with smaller bias and smaller root of
mean squares error. Thus, proposed method proves its superiority over the existing
techniques.
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