Robust multivariate outlier labeling

Dyah E. Herwindiati*, Maman A. Djauhari, Muhammad Mashuri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

A criterion for robust estimation of location and covariance matrix is considered, and its application in outlier labeling is discussed. This method, unlike the methods based on MVE and MCD, is applicable to large and high-dimension data sets. The method proposed here is also robust and has the same breakdown point as the MVE- and MCD-based methods. Furthermore, the computational complexity of the proposed method is significantly smaller than that of other methods.

Original languageEnglish
Pages (from-to)1287-1294
Number of pages8
JournalCommunications in Statistics Part B: Simulation and Computation
Volume36
Issue number6
DOIs
Publication statusPublished - Nov 2007
Externally publishedYes

Keywords

  • Breakdown point
  • Covariance matrix
  • Location
  • Outlier labeling
  • Robust estimation
  • Vector variance

Fingerprint

Dive into the research topics of 'Robust multivariate outlier labeling'. Together they form a unique fingerprint.

Cite this