On the identification of extreme outliers and dragon-kings mechanisms in the upper tail of income distribution

Muhammad Aslam Mohd Safari, Nurulkamal Masseran, Kamarulzaman Ibrahim

Research output: Contribution to journalArticle

Abstract

The presence of extreme outliers in the upper tail data of income distribution affects the Pareto tail modeling. A simulation study is carried out to compare the performance of three types of boxplot in the detection of extreme outliers for Pareto data, including standard boxplot, adjusted boxplot and generalized boxplot. It is found that the generalized boxplot is the best method for determining extreme outliers for Pareto distributed data. For the application, the generalized boxplot is utilized for determining the exreme outliers in the upper tail of Malaysian income distribution. In addition, for this data set, the confidence interval method is applied for examining the presence of dragon-kings, extreme outliers which are beyond the Pareto or power-laws distribution.

Original languageEnglish
JournalJournal of Applied Statistics
DOIs
Publication statusAccepted/In press - 1 Jan 2019

Fingerprint

Box plot
Income Distribution
Outlier
Tail
Extremes
Pareto
Pareto Law
Interval Methods
Power-law Distribution
Confidence interval
Outliers
Distribution of income
Simulation Study
Modeling

Keywords

  • extreme outliers
  • generalized boxplot
  • Income distribution
  • Pareto model
  • probability integral transform statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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