Sensitivity analysis of nongaussianity by projection pursuit

Yufen Huang, Ching Ren Cheng, Tai Ho Wang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

From the information-theoretic point of view, the Gaussian distribution is the least structured. Therefore, the most non-Gaussian direction in which to explore the clustering structure of data is considered to be the most interesting linear projection direction when applying projection pursuit. Non-Gaussianity is often measured by kurtosis. However, kurtosis is well-known to be sensitive to influential points/outliers and so the projection direction can be unduly affected by abnormal points. In this paper, we focus on developing influence functions of projection directions in order to detect abnormal observations, especially on highdimensional data. For multivariate data, a new technique is proposed for defining and developing influence functions of projection directions. In addition, a new influence function is suggested. Two simulated data examples and one concrete data example are provided for illustration.

Original languageEnglish
Pages (from-to)1713-1733
Number of pages21
JournalStatistica Sinica
Volume21
Issue number4
DOIs
Publication statusPublished - 2011 Oct

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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