Influence analysis of non-Gaussianity by applying projection pursuit

Yufen Huang, Ching Ren Cheng, Tai Ho Wang

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

The Gaussian distribution is the least structured from the information-theoretic point of view. In this paper, projection pursuit is used to find non-Gaussian projections to explore the clustering structure of the data. We use kurtosis as a measure of non-Gaussianity to find the projection directions. Kurtosis is well known to be sensitive to influential points/outliers, and so the projection direction will be greatly affected by unusual points. We also develop the influence functions of projection directions to investigate abnormal observations. A data example illustrates the application of these approaches.

Original languageEnglish
Pages (from-to)1515-1521
Number of pages7
JournalStatistics and Probability Letters
Volume77
Issue number14
DOIs
Publication statusPublished - 2007 Aug

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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