Bootstrapping spatial median for location problems

Myoungshic Jhun, Seungjun Shin

Research output: Contribution to journalArticlepeer-review


In multivariate location problems, the sample mean is most widely used, having various advantages. It is, however, very sensitive to outlying observations and inefficient for data from heavy tailed distributions. In this situation, the spatial median is more robust than the sample mean and could be a reasonable alternative. We reviewed several spatial median based testing methods for multivariate location and compared their significance level and power through Monte Carlo simulations. The results show that bootstrap method is efficient for the estimation of the covariance matrix of the sample spatial median. We also proposed bootstrap simultaneous confidence intervals based on the spatial median for multiple comparisons in the multi-sample case.

Original languageEnglish
Pages (from-to)2123-2133
Number of pages11
JournalCommunications in Statistics: Simulation and Computation
Issue number10
Publication statusPublished - 2009 Nov 1
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by a Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2007-314-C00039).


  • Bootstrap
  • Multivariate location
  • Simultaneous confidence interval
  • Spatial median

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation


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